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开发阶段案例 · 模型盲评尚待独立人工复核，不代表正式 Benchmark 结论。
自动补全持平8 / 162 · bc30ba197adadd03

Metagenomic Analysis using Phylogenetic Placement -- A Review of the First Decade

定量生物学 · 2202.03534v2

FLOWING EVIDENCE BENCHMARK

怎么判断 Evidence 真的有帮助？

核心问题是：在同一写作位置、使用同一模型和任务时，提供检索文献片段会怎样改变首次输出？我们成对比较两种条件，保留持平、不可用和评审未完成的结果。

同一段原稿 · 比较是否提供 Evidence

01 · 固定写作位置

论文原稿

同一处写作点位 ▌

同一原稿位置的自动补全成对比较

02 · 构造两组输入

两组共用

原稿上下文、模型、任务和提示词

A · 提供 Evidence

额外提供检索文献片段

B · 不提供 Evidence

不提供检索文献片段

LLM

同一模型、同一版本

A → 首次输出

B → 首次输出

盲评 Agent

匿名标记两份首次输出为 X、Y

按续写质量判断：准确性、任务贴合度和可用性
输出：X 更优 / 持平 / Y 更优 / 两组均不可用

换序复评：X / Y → Y / X

盲评具体怎么判？

① 匿名两份输出
评审看到同一原稿和两份首次输出，但不知道哪份用了 Evidence。

② 比较并换序复评
根据当前任务的标准，按 X/Y、再按 Y/X 的顺序各评一次。

③ 复核分歧
两次结论不一致时再做第三次判定；未完成的评审也留在分母。

自动补全点位怎样分层？

在查看生成结果前，先核查检索片段是否含有能直接支撑下一步续写的具体命题；有则放在左侧机会组，否则放在右侧普通组。每个学科从可准入论文中均衡选取两组点位。50/50 是实验设计，不代表真实写作中两类点位各占一半。

AUTOCOMPLETE · 110

定量生物学、统计学、天体物理学

左侧 55 个、右侧 55 个点位；统计学采用 10 篇论文的复测结果。

AUTOCOMPLETE · 52

心理学与气候科学

左侧 26 个、右侧 26 个点位；心理学计入 9 篇，气候科学计入 4 篇。

表格里的百分比怎么算？

五学科共有 81 个左侧点位，其中 51 个点位的 Evidence 版本获评更优。持平、两组均不可用和评审未完成的点位仍计入分母。

51Evidence 版本更优
÷
81该层全部点位
=
63%该层 Evidence 获评更优的比例

来源贡献是另一项复核：已进入复核的 22 个 Evidence 获胜点位中，16 个确认直接使用了检索论文；另有 29 个胜出点位尚待复核。

当前是开发阶段的模型评审结果，尚未完成独立人工复核；这些数字不代表正式 Benchmark 结论，也不能单独证明因果关系。

原稿写作位置

原文摘录 · 非 PDF 页面

研究论文 · 原文片段

Metagenomic Analysis using Phylogenetic Placement -- A Review of the First Decade

4 Conclusion and Outlook

…created by, and shared in, research communities that investigate the same group(s) of organisms. This would not only yield obtaining high-quality reference trees trivial, but would also immensely increase the comparability across studies, as well as their reproducibility. Consequently, we would highly encourage such collaborations, and the public sharing of (perhaps even versioned instances of) gold-standard reference trees. Notably, for some environments, first efforts into this direction have already been undertaken Berney2017 ; DelCampo2018 ; Rubinat-Ripoll2019 ; Rajter2021 ; Rajter2021a .

Furthermore, as mentioned, there is a lack of established methods that evaluate placement quality in a standardized and meaningful way. In particular, robust metrics are missing to distinguish the case where reference sequences of known species are missing from the tree from the case where the placed data actually contains yet undescribed species. A classification based on the LWR and pendant length of the placement locations might offer a solution here.

↓ 此处生成下一段续写

黄色标出写作点位附近的原文；本页根据评测输入重排文本，不是原始 PDF 截图。

同一写作位置的成对对照盲评结果：持平

两组使用同一模型、同一写作任务和原稿上下文，仅比较是否提供检索文献片段。下方展示评测记录中的首次输出。

Evidence

同一模型与写作位置 · 提供检索文献片段 · 首次输出

Beyond such classification, the field would benefit from a shared benchmark that reports placement quality under a common set of reference trees and query datasets, so that methods can be compared across studies rather than within individual evaluations.

提供给 Evidence 版本的文献片段

pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree

The posterior probability has a clear statistical interpretation as the probability that the fragment is correctly placed on that edge, assuming the reference tree, the alignment, and the priors on pendant branch length. Because the reference tree is fixed, direct numerical quadrature over the likel…

展开完整文献摘录

The posterior probability has a clear statistical interpretation as the probability that the fragment is correctly placed on that edge, assuming the reference tree, the alignment, and the priors on pendant branch length. Because the reference tree is fixed, direct numerical quadrature over the likelihood function can be performed to obtain the posterior probability rather than relying on Markov chain Monte-Carlo procedures as is typically done in phylogenetics [ 44 , 45 ] . In maximum likelihood (ML) mode, pplacer evaluates the “likelihood weight ratio,” [ 39 ] i.e. the ML likelihood values across all placement locations normalized to sum to one.

无 Evidence

同一模型与写作位置 · 不提供检索文献片段 · 首次输出

Beyond such classification, the field would benefit from a shared benchmark that reports placement quality under a common set of reference trees and query datasets, so that methods can be compared across studies rather than within individual evaluations.

模型盲评结论

模型盲评没有判定任一组更优。

查看模型评审原始理由（英文）

Both outputs are identical. They provide a coherent, well-grounded continuation that logically extends the draft's argument: the draft discusses missing standardized methods for evaluating placement quality and proposes LWR/pendant length classification as a potential solution; both outputs add a complementary and natural next step (shared benchmarks for cross-study comparison) without inventing methods, results, or unsupported claims. No citation markers are generated. The continuation is consistent with the draft's scope and the source's context about pplacer's LWR metric, without overreaching into unsupported territory.

文献片段是输入材料；出现于此不代表输出使用了它，也不代表它能够支持全部主张。原文与检索片段经过截取；页面没有展示模拟分数或模拟 PDF。