[The first stage of toxicology evaluation and analysis of 1502 pesticide samples].
Bibliographic record
Abstract
OBJECTIVE: To analyze the results of the first-stage toxicological evaluation of 1 502 pesticide samples. METHODS: The classification of the 1 502 pesticide samples was analyzed, and the experimental results of the samples in different years were compared. RESULTS: Most of the 1 502 pesticide samples were insecticides, accounting for 52.5% of all, followed by bactericides and herbicides. In the 5 years, the proportion of biogenic insecticides showed a significant rising trend (χ² = 11.426, P < 0.05). The proportion of single pesticides was 65.8%; mixed pesticides accounted for 32.7%; original pesticides accounted for only 1.5%. From 2008 to 2012, most pesticides had low toxicity, regardless of the exposure route (via the mouth, skin, or respiratory tract). Acute oral and dermal toxicity tests showed that pesticides with moderate toxicity declined year by year (oral exposure χ² = 18.036, P < 0.01; dermal exposure χ² = 40.482, P < 0.01). There was a small proportion of pesticides with high toxicity. We did not detect any pesticide with extreme toxicity. Acute skin irritation and eye irritation test showed an upward trend in proportion of non-irritating pesticides (χ² = 77.110, P < 0.01; χ² = 12.693, P < 0.05), while the proportion of medium-irritation pesticides decreased significantly (χ² = 18.941, P < 0.01; χ² = 13.129, P < 0.05). Sensitization test showed that all samples were weak sensitizers. CONCLUSION: The major type of investigated pesticides was insecticide. Most samples were single pesticides, and there was a certain proportion of mixed pesticides. Novel pesticides such as bio-pesticides are the development tendency. The tested pesticides were mainly low-toxicity pesticides, with a certain proportion of medium- and high-toxicity pesticides. Personal protection should be strengthened during production and use of pesticides.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".