Challenges faced by the IR‐4 Programme and US specialty crop growers
Bibliographic record
Abstract
The Food Quality Protection Act (FQPA) was enacted in August 1996 and required the US Environmental Protection Agency (EPA) to reassess all existing and new crop protection active substances using a new set of health and environmental standards to further protect infants and children. The initial fear that many minor or specialty crop use registrations would be lost without adequate replacements has largely been overcome by an aggressive programme by the International Research Project no. 4 (IR‐4) in partnership with the EPA and the crop protection industry to register new, safer, reduced risk products for specialty crop pest control needs. Since the FQPA, the EPA has approved over 5600 new specialty crop uses resulting from IR‐4 residue programmes. This amounts to about 56% of the over 10 000 clearances received by the IR‐4 programme in its 43 year history and about 50% of all new uses granted by the EPA since FQPA. The positive outcomes from these efforts have been partially negated by the lack of tolerances or Maximum Residue Levels (MRLs) in countries to which US produce is exported. This has forced some US specialty crop growers to continue to use older, less desirable products. IR‐4 has been addressing this challenge by cooperating in the NAFTA (North American Free Trade Agreement) countries with Agriculture and Agri‐Food Canada's Pest Management Centre and Health Canada's Pest Management Regulatory Agency to harmonize MRLs through joint projects and regulatory reviews. IR‐4 has also provided leadership for the International Crop Grouping Consulting Committee to harmonize specialty/minor crop groupings and representative crops for residue studies with the long‐term goal being to globally harmonize MRLs.
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 | 0.042 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".