Bibliographic record
Abstract
Abstract Risk assessment of pesticides has its roots in the same process for chemicals in general, both of which are relatively recent. Pesticides such as oxides of sulfur and some minerals were also used by early civilizations but the concepts of dose-response and risk as a probability were only documented in the literature (books) in the 1500s and 1600s, respectively. Formal use of toxic dose and safety factors for humans was developed as inorganic and organic pesticides entered the market after the 1930s, but only made use of simple hazard ratios to characterize danger. This approach continued until adoption of the concept of probability of exposure of humans to pesticides via dietary exposure, but not sensitivity of humans. It was in 1980s–90s that the use of probability was suggested as a way of characterizing variation in sensitivity of species in the environment as well as the exposures in environmental matrices. As we move into the future, risk assessment of agrochemicals will evolve to include new frameworks and approaches for dealing with conflicting data, such as Weight of Evidence.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".