Additional file 1 of Characterization of antibiotic resistance genes in drinking water sources of the Douhe Reservoir, Tangshan, northern China: the correlation with bacterial communities and environmental factors
Bibliographic record
Abstract
Additional file 1: Table S1. GPS location of sampling locations. Table S2. Water quality of the selected samples. Table S3. Sediment quality of the selected samples. Table S4. Primer sets used in this study. MLSB (Macrolide–Lincosamide–Streptogramin B), MGEs (mobile genetic elements) and FCA (fluoroquinolone, quinolone, florfenicol, chloramphenicol, and amphenicol). Table S5. Relative mean abundance of each ARG and MGE subtypes in sample (copies/16S rRNA gene). Table S6. Average number of detected ARGs and MGEs of the each sample. Table S7. Diversity indices of bacterial communities in each sample. Table S8. Bacterial phyla percent in each water samples. Bacterial genus percent in each water samples. Bacterial phyla percent in each sediment samples. Bacterial genus percent in each sediment samples. Figure S1. Relative abundance of different bacterial phyla in water and sediment samples.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.408 | 0.065 |
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".