Additional file 2 of MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities
Bibliographic record
Abstract
Additional file 2: Fig. S2.Gene annotation performance of MetaPro, HUMAnN3, and HUMAnN2. Stacked barcharts depicting the number of reads annotated to specific taxa in (A) NOD mouse samples, and (B) Kimchi samples by BWA alignments, MetaPro, HUMAnN3, and HUMAnN2. The NOD mouse datasets were generated from gut samples from mice inoculated with a defined microbial consortium (Altered Schaedler Flora (ASF); [29]). In addition to the 8 taxa associated with ASF, reads were also assigned to Parabacteroides goldsteinii, a close relative of Parabacteroides ASF519 (see legend). The kimchi datasets comprise five major taxa (see legend; [30–34]). It should be noted that Leuconostoc gasicomitatum reported in the original publication is currently classified as a subspecies of Leuconostoc gelidum. For NOD sample SRR1828965, HUMAnN3 did not annotate any reads; for kimchi sample SRR443366, HUMAnN2 did not annotate any reads.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.664 | 0.180 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".