Additional file 1 of Triadimefon in aquatic environments: occurrence, fate, toxicity, and ecological risk
Bibliographic record
Abstract
Additional file 1. Data on exposure to triadimefon, expressed as concentrations in surface waters including rivers, lakes, reservoirs were collected from literature published in China and abroad and unpublished data from recent monitoring results by the authors (Table S1 and Table S2). Data on half-lives to triadimefon were also collected from literature published in China and abroad (Table S3). Toxicity data for the triadimefon were collected from the ECOTOX Knowledgebase developed by US EPA ( https://cfpub.epa.gov/ecotox/search.cfm ), following the principles of accuracy, relevance and reliability (Table S4). Toxicity data based on various assessment end points sets for triadimefon were tested for normality by use of Anderson–Darling text, HC5 and PNEC were derived by curves fitting (Table S5). Finally, sensitivity distributions base on different end points were compared by use of the two-sample Kolmogorov–Smirnov test, using the SPSS software version 26 (Table S6). ECOTOX Knowledgebase. https://cfpub.epa.gov/ecotox/search.cfm Accessed 20 April 2021.
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 | 0.017 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.855 | 0.137 |
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".