Eco-GEO: Supply chain. Ordering visibility for AI cold start: First eliminate solutions that cannot pass the threshold, then compare the total costs of repair vs. expansion coverage.

Core judgment: First, eliminate those who meet the threshold. Then discuss whether to prioritize “pre-existing facts” or expand coverage.

Condition: During a cold start period of 0-1, the channel manager has 80 hours and 8 weeks at his disposal (a indicative budget). Brand-related facts are scattered across old quotation sheets, expired certification pages, and various dealer statements. Additionally, competitors’ references can be found in the AI answers. The choices are: A) First unify the facts and fix expired and incorrect information; B) First conduct issue coverage and content expansion; C) Postpone expanding GEO. All three options share the same budget, observation window, and set of issues.

• the threshold precedes ranking, but the threshold does not equal “A automatically prioritized”. indicates that when m=20 (E_base=0.50), B is excluded first due to a higher baseline error rate than 0.10; whether A is feasible depends on the corrected error elimination rate qA: when qA=0.60, E_A=0.20, and A is also not feasible. The conclusion for this round is C; when qA≥0.80, E_A=0.10, ΔQ_A=8, and A becomes the only feasible option. The conclusion is A.
• The upper limit of the number of repaired objects is determined by both the number of errors and the unqualified spaces, not by man-hours alone. nA=min(floor(52/hA), m, Q_cap). High error rates increase the difficulty of meeting the A standard (more errors need to be eliminated to stay within the threshold), and they may also prevent available objects from being used by m.
• When N=40, A and B will not be qualified simultaneously. For A, if ΔQ≥8, then m≥8/pA must hold; for B, if E_base≤0.10, then m≤0.10N must hold. With pA=0.40, both conditions must be met simultaneously, which requires N≥200. Therefore, the recurring decision in this example is “to do the only feasible option, or to pause”.

Not applicable: For enterprises with no versioned fact ledger, no independent review personnel, unstable issue extraction (N<20), or insufficient qualified space (Q_cap<8), this calculation does not apply. These enterprises should directly proceed to step C and provide additional evidence first. All numerical values in the text are hypothetical and not industry benchmarks or actual measurement results for any enterprise.

1. Mechanism assumptions and falsification methods: Evidence only reaches “qualification and relevance”; it does not reach “effect”.

Google’s search documentation states that AI Overviews and AI Mode may use “query fan-out,” initiating multiple related searches across sub-topics and multiple data sources. This can result in a broader set of links compared to traditional searches. A page must be indexed and eligible to display summaries in searches to become a supported link, with no additional technical requirements. [S1] This document describes eligibility criteria and general practices, but does not promise any brand-specific growth in citations.

由此可以提出的假设是：若同一处品牌参数在多个页面上互相冲突，不同子查询可能命中不同版本，使同一意图的多个答案路径出现矛盾表述。这里的中介变量是“同一事实存在多个公开版本”；可观察证据是固定问题集上的答案是否复述了过期或错误版本；使该解释失败的条件是：即使把所有版本统一，固定问题集上的错误表述数量也不下降。错误会被复制到多条答案路径这一说法，本篇按待检验机制处理，不作为已证实的工程事实。

A v1 preprint on arXiv conducted extensive measurements: covering 55,936 queries, six large-model-based search systems, and two traditional search systems. It reported that 37% of the domain names in its sample appeared only in the large-model search side. The domain names favored by these systems were more structured in terms of features (hierarchical HTML), with easier-to-read text, lower domain popularity, and more external links pointing to high-reputation sources. [S3]These are feature associations, not causal interventions. It cannot be inferred that “more external links pointing to high-reputation sources will increase the probability of being cited.” The check direction it suggests is: if the bottleneck of this brand lies in page structure and readability, there is limited room for further investment in error correction.

The same preprint also cited two existing studies in the literature review: one study suggested that incorrect information in the retrieved documents might spread to the generated answers (Deng et al., 2025); another study argued that users would be influenced by the number and type of citations, even if the attribution was incorrect (Miroyan et al., 2025). [S3] are existing papers cited by S3, not experimental conclusions of S3 itself. This paper does not consider “the existence of citations” as evidence of effectiveness.

Microsoft’s announcement regarding Copilot Search states that the product will highlight sources, link entire sentences or paragraphs in the answers to those sources, and list all the links used to generate the answers. The announcement also notes that available regions include countries other than China and Russia. [S5]This is a manufacturer’s feature statement, not proof of accuracy or translation quality. It also reminds readers in the Chinese market that the same set of actions may vary across different platforms.

OECD 的政策报告主张按两类风险对韧性策略分段：常态化中断由企业常规风险管理处理，极端事件才需要政府作为促进者与应急资源提供者介入，把常规工具用于重大中断会造成浪费与表现不佳。[S2]一篇咨询机构的公开观点文章则指出，韧性投入存在显式取舍，例如集中采购降低复杂度与成本，而多元化供应提高韧性但增加成本与复杂度。[S4]这两份材料都不是 GEO 研究，只能提供方法类比：按条件分段、把代价列全。它们不能支撑任何关于 AI 引用效果的说法。

2. Caliber: Problem sets, two rounds of baseline, and differences between N and Q_cap targets

For example, consider a fixed set of 40 questions (for illustration), with each question representing a sample unit. The questions are selected by channel managers from nearly 12 months of inquiry and sales interactions for this brand, after deduplication, and then frozen in terms of platform, version, language, question type, and sampling period. Operations staff complete two rounds of baseline sampling, and final rechecks are conducted using the same procedures. Each question retains its answer and source link. Independent reviewers determine the answers based on version-specific facts, with disagreements resolved by a third party. Different platforms report separately, without combining denominators.

“Qualified references” require traceable references in both rounds, and the brand-related facts involved in the question must be correct within the agreed verification range. Questions with incorrect brand facts in any round are counted as errors and are not excluded based on severity—severity grading requires separate criteria, which are not used in this article. Error questions do not overlap with qualified questions. The number of baseline error questions is denoted as m, E_base = m/N; Q_base is the integer count of qualified questions (illustrated as 6), Q_cap = N - Q_base = 34, representing the remaining unqualified space. Here, the two quantities must be separated: N is the total number of questions in the pool, with a threshold of 20 questions; Q_cap is the potential improvement space in this round, with a threshold of 8 questions. If either condition is not met, choose C directly for (0), the action is the same, with no difference in order.

• E: Numerator = number of questions containing brand-related factual errors; Denominator = N = 40; Sampling unit = question; Collector = channel operation; Window = two consecutive rounds before intervention; Deviation control = same collector, same question format, same time period; The question set is not modified between the two rounds.
• Q: The numerator equals the number of qualifying questions, the denominator equals N, the sampling unit equals the question, the reviewer equals an independent third person, the window equals the start and end periods within the same observation window, deviation control equals keeping both the answer and the source, and the disagreement rate equals the number of disagreeing questions divided by the number of reviewed questions.
• ΔQ: The difference between the beginning and end of the same period for the same set of questions, with the same denominator definition, and the same window. It is expressed in terms of increment, without using the total amount at the end of the period.

The number of available objects for B is expressed as U = Q_cap − m. This formula holds true under the conditions that all questions have been verified, any factual errors in any brand are counted towards m, and the incorrect questions do not overlap with the correct ones. If these conditions are not met, this formula cannot be used; proof must be provided first. This article does not promise statistical significance; it only ensures consistency and reproducibility. No significance markers are retained in this reading window, so no judgments of “significant/insignificant” can be made regarding any effects.

3. Thresholds and sorting rules: First, eliminate those that do not meet the criteria. Then, compare them. Finally, sort them accordingly.

The pre-frozen indicative threshold (calibration method: first determine the stable level that this brand can achieve through two rounds of baseline testing, then review the results. The threshold must not be changed afterward): N≥20, Q_cap≥8, total planned hours for the project ≤80 hours, E_post≤0.10, ΔQ≥8, review disagreement rate <20%. 0.10, 8 questions, 20 questions are all test values to be calibrated, not industry parameters given by the source.

Sorting rules cover all scenarios in order: (0) If the data conditions do not hold (N<20 or Q_cap<8, or no versioned ledger, no independent review, disagreement rate ≥ 20%), directly choose C. (1) Check the feasibility and stopping conditions for each scenario: If ΔQ expectation ≤ 0, exclude it and do not calculate unit cost; A requires nA ≥ 8/pA (equivalent to ΔQ_A ≥ 8) and E_A ≤ 0.10 is expected; B requires E_base ≤ 0.10 and nB ≥ 8/pB (equivalent to ΔQ_B ≥ 8). (2) If only one scenario is feasible, choose it; if none are feasible, choose C. (3) If both are feasible, compare the complete unit cost H/ΔQ; choose the lower one; if costs are equal, choose the one with a higher increment; if increments and costs are equal, conduct supplementary testing before making a decision; do not force a choice.

两类等号要分开写清。门槛处的等号：E_post＝0.10、ΔQ＝8、N＝20、Q_cap＝8、总工时＝80 都按“达到即通过”处理；复核分歧率是严格不等——分歧率＝20% 视为不通过，与第 2 节“≥20% 暂停扩量”和 (0) 一致。排序处的等号属另一条规则：单位成本相等时取 ΔQ 较高者，两者都相等时补测后再判。两处等号分属不同规则，不可互相套用。

(3) In this example, the condition cannot be triggered when N=40: the requirement for B to be eligible is m≤0.10N=4, while ΔQ_A≤m×pA≤m≤4<8. Therefore, A never meets the criteria. To make (3) triggerable, m must be ≥8/pA (i.e., m≥20 when pA=0.40) and ≤0.10N, meaning N≥10m≥200. Thus, (3) is a rule designed for larger problem pools; any changes to parameters must be re-evaluated and cannot be copied directly.

Conversely, for A’s feasible range (with parameters pA=0.40, qA=0.80, hA=1.6, N=40): ΔQ_A≥8 requires m≥20, while E_A=m(1−qA)/40≤0.10 requires m(1−qA)≤4. When qA=0.80, m must be equal to 20. By adjusting qA to 0.90, m can range from 20 to 32. This explains why “higher error rate means immediate repair” is incorrect: if m is too small, it cannot meet the increment threshold; if m is too large, it cannot meet the error rate threshold.

Write the rules as executable statements, corresponding to (0)(1)(2)(3) item by item, without any additional prerequisites:

• Step 0: If N<20, or Q_cap<8, or there is no versioned fact ledger, or there is no independent review, or the disagreement rate during review is ≥20%, choose C. The process ends.
• Step 1: Determine separately whether A and B are feasible. A is feasible if ΔQ_A≥8 and E_A≤0.10; B is feasible if E_base≤0.10 and ΔQ_B≥8. If either scenario’s expected ΔQ≤0, it is immediately deemed infeasible, without considering unit costs.
• Step 2: Neither A nor B is feasible → C; only A is feasible (for example, E_base>0.10 makes B unfeasible, or nB<8/pB → A; only B is feasible (A is unfeasible due to nA<8/pA or E_A>0.10) → B.
• Step 3: Both A and B are feasible → Compare the complete unit cost H/ΔQ; choose the lower value first. If costs are equal, choose the one with higher ΔQ. If both costs are equal, make a supplementary measurement before making a decision.

According to this sequence, the scenario m=20 in Table 2, where qA/pA/hA/S consists of four rows, yields A: Here, E_base=0.50 makes B deemed infeasible in step 1. In step 2, A becomes the only feasible option—this step does not rely on E_base≤0.10.

4. Simplified ledger: How 80 hours can be closed in two scenarios

Joint preliminary work (both A and B must be paid, and it counts towards both sides’ total costs): Fact ledger 10 hours + two rounds of baseline and final sampling totaling 12 hours + independent review and adjudication 6 hours = 28 hours; remaining intervention capacity 52 hours. Joint work only involves organizing judgment bases and measurements; no changes are made to the public page. This model does not account for any additional hours. The two plans are alternative trials, not separate 80-hour increments.

A: hA = 1.6 hours/problem (Verify: 0.8 + rewritten and submitted 0.8, indicative split); nA = min(floor(52/hA), m, Q_cap); ΔQ_A = nA × pA; E_A = (m − nA × qA)/N; H_A = 28 + hA × nA; Constraint: 0 ≤ pA ≤ qA ≤ 1 (Eliminating errors does not necessarily lead to citations). B: Only handle problems where the facts have been verified correctly but still not deemed qualified for citation; hB = 2.0 hours/problem; U = Q_cap − m; nB = min(floor(52/hB), U); ΔQ_B = nB × pB; E_B = m/N; H_B = 28 + hB × nB. E_B in B is a model assumption, assuming that B neither eliminates existing errors nor creates new ones; at the end of the period, re-sampling is required. If B introduces new errors, it will be handled according to E_post>0.10, meaning B is also subject to the 0.10 threshold. If B must complete A first, then B’s cost must include repair hours and the recalculated common baseline. This cannot be applied to the independent account of B.

High error scenario (example): m=20, Q_cap=34, pA=0.40, qA=0.60, hA=1.6 → nA=min(32,20,34)=20; ΔQ_A=8; Q_end=6+8=14; E_A=(20−12)/40=0.20; H_A=28+32=60. Budget closure: 10+12+6+32=60. B is excluded in step 1 due to E_base=0.50; ΔQ_B and unit cost are not calculated. Conclusion C: A also does not exceed the error rate threshold. If qA is calibrated to 0.80, then E_A=(20−16)/40=0.10, ΔQ_A=8, and A becomes the only viable option; the action is set to A.

Low error scenario (example): m=2, U=32, hB=2.0, pB=0.25, pA=0.40, qA=0.60 → A: nA=min(32,2,34)=2, ΔQ_A=0.8, E_A=(2−1.2)/40=0.02 (meets error threshold but increment is insufficient), H_A=28+3.2=31.2, unit cost = 31.2/0.8=39; B: nB=min(26,32)=26, ΔQ_B=6.5, Q_end=12.5, E_B=0.05 (hypothetical, requires re-measurement), H_B=28+52=80, unit cost = 80/6.5≈12.31. Both ΔQ<8 are not feasible → C. The values 39 and 12.31 here are for completeness only; the threshold has excluded both options, so cost comparison no longer affects the decision.

Integer execution: nA and nB are the numbers of integer operations; ΔQ is the model expectation, which can be a decimal and cannot be treated as an observed count. Final acceptance by integers: The number of incorrect and correct questions is counted as actual counts. Q_end must be at least 8 more questions than the baseline (Q_base=6 → Q_end≥14); if the original correct questions are lost, it is calculated based on the net increase, and only the newly obtained questions should be counted.

The remaining working hours are accounted for using conditional rules: if a plan uses less than 52 hours of intervention capacity (for example, nA=20 uses only 32 hours), the remaining working hours are filled in according to pre-established rules by filling in missing fields in the fact ledger (each field is assumed to be 1.0 hours). If there are no missing fields to fill, they are recorded as unused and reassigned in the next budget cycle. It is not allowed to calculate costs based on all 52 hours while ignoring the remaining capacity. The budget closure sentence in Section 7 follows the same criteria as this section.

5. Table 2: How the single-variable critical values change the behavior

In Table II, only one input per row should be changed; the rest should remain unchanged. The low and high values represent demonstration ranges, not measurement intervals. The result column shows the model’s expected values and corresponding actions (A/B/C).

For each item, modify only one input at a time: qA is derived from (20−20qA)/40≤0.10, resulting in qA≥0.80. Taking 0.79 gives E=4.2/40=0.105, which fails. Taking 0.80 gives 0.10, which passes. Taking 0.90 gives 0.05, which also passes. pA is derived from 20pA≥8, resulting in pA≥0.40. With 0.39, ΔQ=7.8 fails. With 0.40, it’s 8, which passes. With 0.60, it’s 12, which also passes. Under this combination, E is always 0.10 (since qA is fixed at 0.80). pB is derived from 26pB≥8, resulting in pB≥4/13≈0.3077. When equal, ΔQ_B=8, Q_end=14, H_B=80, unit cost 80/8=10, E_B=0.05 → B. With 0.30, ΔQ=7.8 fails → C. On the capacity side, also modify only one input: hA=2.60 yields nA=20, which passes. hA=2.61 yields 19, ΔQ=7.6 fails. hB=1.625 yields nB=32, which passes. hB=1.63 yields 31, ΔQ=7.75 fails. S=48 yields nA=20, H_A=80, which passes. S=49 yields nA=19, ΔQ=7.6 fails.

When combining the high and low sides, the action does indeed flip. For combination one (m=20 side), taking qA=0.50 and pA=0.20, with everything else fixed: A’s E_A=(20−10)/40=0.25>0.10, ΔQ_A=4<8. A is not feasible; B is excluded by E_base=0.50 → C. In the same scenario, setting qA to 0.90 and pA to 0.60: E_A=(20−18)/40=0.05≤0.10, ΔQ_A=12≥8 → A. For combination two (m=2 side), taking pB=0.15: ΔQ_B=3.9<8 → C; taking pB=4/13: ΔQ_B=8 → B (at this point, A is not feasible due to ΔQ_A=0.8<8). In other words, there are only two scenarios that truly change A/B/C: when the conversion rate exceeds limits (qA, pA, pB rows) causing C to become A or B; when the capacity exceeds limits (hA, hB, S rows) causing A or B to become C.

By the way, for the calculation examples, the algebraic intersection points where the expected increments of the two solutions are equal are: pB = ΔQ_A/nB = 0.8/26 ≈ 0.0308. Substituting 0.02, 0.0308, and 0.05 gives ΔQ_B = 0.52, 0.8, and 1.3 respectively. All these values are less than 8, so option C is chosen for all three cases. This intersection point is far below the range of 0.15–0.55 mentioned in this demonstration; it has no practical significance and cannot be considered an A/B inversion point. The critical threshold for low-error scenarios is pB ≥ 4/13 (under the conditions of nB = 26 and hB = 2.0). Additionally, the calculated values of 39 and 12.31 hours/correct increment for low-error situations are merely for completeness and not used as a basis for selection—since both solutions’ ΔQ values are below the 8-point threshold, cost comparisons do not change the outcome.

In this example with N=40, no single variable change makes A and B feasible simultaneously. Therefore, there is no situation where changing one variable switches the preferred option from A to B, nor is there a need to compare unit costs according to quadrant (3). This is not a model flaw; rather, it’s a result of the combination of three conditions: N=40, ΔQ≥8, and E_base≤0.10. For quadrant (3) to be triggered, there must be at least 200 problems in the problem pool.

6. The most threatening opposing interpretations and invalidation conditions

反方解释一：本品牌引用不足主要来自可读性与结构，而不是事实错误。S3 记录的特征里本来就包含更结构化的 HTML 与更易读的文本，[S3]所以这个解释与本篇的证据并不冲突。检验方式：对每个未合格题逐项记录原因（事实缺失／事实错误／结构或可读性／缺少第三方证据），允许多原因共存，并在两轮基线中记录各类原因的题数占比。这条诊断不新增门槛，也不改变 (0)(1)(2)(3) 的先后顺序：若 A 试点后 ΔQ_A 仍低于 8 题，(1) 已把 A 判为不可行，无论原因怎么归类，动作都不会变成 A；原因归类的作用是决定下一次试点指向 B 还是结构优化，而不是覆盖门槛。若修复后错误率下降但净引用增量仍为 0，则“纠错带来增量”的链条被推翻，停止扩量。若原因归类中事实类占优，则 A 的机制前提在本品牌场景得到部分支持，但动作仍以 (0)(1)(2)(3) 为准，本篇不为该诊断单设阈值。

Opposition Explanation 2: The real bottleneck is that the problem pool is too small (N<20 or Q_cap<8). In this case, neither A nor B is reliable. Any sorting is based on noise, so it should be switched to C. First, prove the evidence before discussing investment. Opposition Explanation 3: The opportunity cost of offline actions is lower. This article does not provide a conversion rate between “yuan/effective clues” and “hours/qualified references.” It also does not argue that offline methods are inherently better. If the team has separate records of effective clues for offline actions, then set separate targets and compare them separately, without mixing them with the citation increment mentioned in this article. C is implemented according to the sequential rules of steps 0, 1, and 2 in this article, rather than through cross-dimensional comparisons of unit costs.

Observations that can refute the conclusions of this article: The pilot project calibrates qA and pA above the critical point, and with a sufficiently large m, makes A the only viable option; or calibrating hB and pB at 1.625 and 4/13 respectively, makes B feasible from C; or there may be larger problem sets with N≥200 and m≥20, making both A and B feasible simultaneously. In such cases, the complete unit cost must be compared according to Section 3(3). This reading window does not retain any significance markers. S3 is a v1 preprint, and its complete method and significance cannot be confirmed. This window cannot confirm significance, nor can it assert that any effects are insignificant.

7. Execution path, responsible persons, and budget closure

Week 1 (Responsibility of channel leaders; 1 person from product and compliance signed): Freeze issue sets, fields, thresholds, and ledger templates. Indicator = Completion rate of ledger fields = Number of verified fields / Number of planned fields; <80% → directly choose C. Weeks 1–3 (1 person for data collection, 1 person for independent review, third party for arbitration of disagreements): Complete two rounds of baseline testing. Indicators = E_base, Q_base (reported separately by platform), disagreement rate; if the directions from two rounds are inconsistent or the disagreement rate ≥ 20%, then choose C and revise the evaluation criteria first. Weeks 3–7: Conduct no more than 10 trials within the remaining 52 hours, calibrating at least one of pA, qA, pB, hA, hB, S; the trial results will be included in nA or nB, with sampling and review counted as 28 hours, not additional output. If no available trials can be obtained, only calibration will be done, no expansion approved. Weeks 7–8: Repeat the evaluation process and finalize results. Indicators = ΔQ, E_post, H, unit cost.

The expansion of conditions is determined sequentially: (i) The selected plan must have measured E_post≤0.10 and ΔQ≥8 simultaneously; (ii) The plan is the only feasible option in step 2, or its total unit cost H/ΔQ is lower than that of another feasible option in step 3. Here, “lower unit cost” refers to comparison with another feasible option, not with alternative uses. Expansion does not occur if only one condition is met: if E_post>0.10 and ΔQ≥8, it indicates that qA is below expectations. Only by adjusting qA to ≥0.80 can the plan meet the standard again; otherwise, proceed to C. If ΔQ<8 and E_post≤0.10, it means that pA or capacity is below the threshold (pA<0.40, or hA>2.60, S>48). Only after these inputs are adjusted to the threshold can the process be restarted; otherwise, proceed to C.

The stopping condition is independent of the expansion condition. Check in order: (a) If the team records the unit cost for alternative uses under the same unit (hours/correctly referenced), and the selected option is higher than that, then stop and proceed to C; (b) If alternative uses cannot be converted to the same unit (for example, offline actions are only recorded in yuan/effective leads), no conversion rate will be provided in this case. This comparison record is a qualitative judgment, which will be determined in writing by the responsible person during the next budget review round. It does not participate in the automatic determination of this round.

Budget item closure: Ledger 10 + Sampling 12 + Review 6 + Intervention hours + Unused hours = 80 hours. In high-error scenarios, it’s 10+12+6+32=60 hours. The remaining 20 hours are handled according to section 4: if there are missing fields in the ledger, they are allocated (each item is counted as 1.0 hours). If there’s no space, they are clearly recorded as unused and reassigned in the next budget cycle. If determined to be C, the 28 hours already spent cannot be recovered; the remaining 52 hours are not recorded as GEO intervention expenses. The schedule is recommended based on this example’s hours, not a conclusion from the source. If capacity is insufficient, decisions can be delayed or trials can be scaled down, but the review step cannot be removed.

8. Assumptions, boundaries, and non-movable parts

Time and version: The data packet was read on 2026-09-29T11:45Z (UTC; S1 11:45:18, S2 and S3 11:45:22, S4 and S5 11:45:24). This text does not convert to local dates in other time zones. S3 is an arXiv v1 preprint, and the reading window was truncated, making it impossible to confirm the complete method and significance. The “published” fields for S1, S2, S3, and S5 are empty. S4 is recorded as 2022-10-04, and its publication date is not inferred from links or titles. Platform documentation may be updated, and conclusions are based on its current version.

缺乏垂直证据：没有中国供应链买家的 AI 搜索行为数据，没有本品牌或任何企业的 GEO 转化实测，没有采访或客户案例。m、pA、qA、pB、hA、hB、S 以及 0.10、8 题、20 题、80 小时/8 周全部为示意参数；校准方式是两轮基线加不超过 10 题的试点，若无法完成校准，则只保留顺序规则、不给出任何数值性推荐。

An explicit assumption: The buyer’s target issues in this scenario are focused on parametric facts such as specifications, certifications, delivery timelines, MOQ, origin, and payment conditions. This comes from the scenario setup of this article; it is not evidence from sources and must be verified with a real list of procurement problems. What can be transferred is the order of calculation—first identifying the total amount and the gap from standards, then limiting work hours based on actual quantities, checking error rates and incremental thresholds, and comparing complete unit costs. If insufficient, pause and clarify where the budget goes. What cannot be transferred are any specific numbers, industry parameters, or conclusions about effectiveness. S2 and S4 only provide a method of “segmenting by conditions and listing all costs,” which does not constitute a basis for GEO investment.

Invalid conditions: If the proportion of factual reasons is low, the error rate does not decrease after unification, the error rate decreases but the net citation increase remains 0, or actual testing shows that neither A nor B can cross the threshold within 80 hours, then the corresponding branch of this article automatically becomes invalid, and the process returns to step C.