Additional file 1 of The global distribution and environmental drivers of the soil antibiotic resistome
Bibliographic record
Abstract
Additional file 1: Supplementary Figure 1. Extrapolation of uncertainties associated with the global survey used in this study. Supplementary Figure 2. Main structure for the a priori structural equation model used in this study. Supplementary Figure 3. Structural equation models assessing the direct and indirect effects of environmental factors on the proportion (A) and richness (B) of ARGs in natural ecosystems only (i.e. croplands excluded, n = 802). Supplementary Figure 4. Global precipitation (A) and temperature (B) seasonality maps used in our study ( https://www.worldclim.org/data/index.html ). Supplementary Table 1. List of antibiotic resistance genes (ARGs) mobile genetic elements (MGEs) considered in this study. Supplementary Table 2. Biomes included in this study. The biome classification was done based on vegetation field information and climatic information from the Köppen classification16. Supplementary Table 3. Environmental factors included in our structural equation model. Supplementary Table 4. Standardized direct effects of SEM on the proportion of soil ARGs. Supplementary Table 5. Standardized direct effects of SEM on the richness of soil ARGs.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.773 | 0.150 |
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".