“Selective” Pesticides: Are They Less Hazardous to the Environment?
Bibliographic record
Abstract
For half a century, scientists and the public have been well aware of the risk posed by pesticides to humans and the environment. Worldwide concern about pesticide residues on food and in drinking water has led to legislative efforts to restrict the use of traditional, broad-spectrum pesticides. In the United States, the Food Quality Protection Act (Public Law 104–170), passed by Congress in 1996, effectively mandates a severe reduction in the use of many such pesticides for a wide range of agricultural uses. The principal rationale for restricting the use of many of these chemicals is to protect consumers, especially children, who are judged to be more susceptible to the effects of pesticides (NRC 1993, Goldman 1998). For their part, in anticipation of the loss of many widely used organophosphorus and carbamate insecticides, pesticide producers have developed a suite of new biorational pesticides designed to target only select organisms. These new products are typically termed selective based on the results of simple laboratory dose–response trials with target and nontarget species to determine LD50, the dose of a chemical that kills 50% of the population tested (LD = “lethal dose”). There is increasing evidence that many nontarget species are affected by several of these chemicals in ways that are often surprising and unpredictable (Banken and Stark 1998, Boyd and Boethel 1998, Losey et al. 1999, Smith and Krischik 1999).
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".