Additional file 2 of Degradation pathways for organic matter of terrestrial origin are widespread and expressed in Arctic Ocean microbiomes
Bibliographic record
Abstract
Additional file 2: Table S1. Metadata and physicochemical properties of the samples collected for this study. Table S2. List of metabolic pathways (obtained from metacyc) involved in the degradation of lignin-derived aromatic compounds and their EC numbers. KO numbers, protein names and enzyme functions information is included for reactions identified as markers for these pathways. Table S3. Properties of the 46 MAGs most implicated in the degradation of aromatic compounds. Genome size, completeness, contamination, N50 values, number of genes and GC content were obtained using CheckM. The GTDB taxonomy was obtained using the nearest placement in a phylogenetic tree with the GTDBTk. The NCBI taxonomy was obtained using the nearest placement in a phylogenetic tree with CheckM.Table S4. Information and sources of genomes from other studies used in this study. Table S5. Properties of the 16 Alphaproteobacteria MAGs most implicated in the degradation of aromatic compounds and representative of the genomospecies, as well as the 12 publicly available genomes identified as their closest relatives. Table S6. Data availability.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.598 | 0.114 |
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".