Additional file 2 of Distinctive signatures of pathogenic and antibiotic resistant potentials in the hadal microbiome
Bibliographic record
Abstract
Additional file 2. Datasets S1: metagenomic data information. Dataset S2: key words which were used to extract the hmm models to construct the ARGfams from Pfam (v 30.0) and TIGRFAMs. Dataset S3: ARG types and subtypes' information of domain based annotation result. Dataset S4: MGE types and subtypes' information. Dataset S5: The number of toxin genes predicted from the sediment metagenomes based on DBETH. Dataset S6: The number of toxin genes predicted from the sediment metagenomes based on VFDB. Dataset S7: Relative abundance of known ARGs in the sediment metagenomes based on ARGs-OAP v2.0 pipeline analysis. Unit: copies per 16S rRNA gene copy (GP16S). Dataset S8: Number and distribution of MGEs genes predicted from the sediment metagenomes based on MGEfams. Dataset S9: Number of ARGs predicted from the sediment metagenomes based on ARGfams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.411 | 0.076 |
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".