Additional file 2 of Large-scale genomic analysis of antimicrobial resistance in the zoonotic pathogen Streptococcus suis
Bibliographic record
Abstract
Additional file 2: Figures S1-S13 and Table S2. Fig S1 – Phylogenetic tree. Fig S2 – MICs in Canada & the UK. Fig S3 - The effects of candidate determinants on MIC for beta-lactams Fig S4 - The effects of candidate determinants on MIC for MLSB. Fig S5 - The effects of candidate alleles on MIC for tetracyclines. Fig S6 - The effects of candidate alleles on MIC for fluoroquinolone. Fig S7 - The effects of candidate alleles on MIC for the aminoglycoside, spectinomycin. Fig S8 - The effects of candidate alleles on MIC for the pleuromutilin, tiamulin. Fig S9 - The effects of candidate alleles on MIC for trimethoprim (TMP). Fig S10 - Variation in the presence of candidate AMR determinants explains consistent differences between genetic clusters. Fig S11 - Methods of using candidate determinants to predict MIC. Fig S12 - Allelic variation in ermB and unidentified sources of epistasis. Fig S13 -High levels of ‘nestedness’ suggest that resistance determinants to beta-lactams are acquired in a particular order. Table S2 - Binomial tests showing that novel AMR variants are independently associated with MIC in different genetic clusters.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.855 | 0.185 |
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".