Scientists’ warning: we must change paradigm for a revolution in toxicology and world food supply
Bibliographic record
Abstract
currently lacks the proper perspective. From the 1950s to the 1970s, at least one-third of all toxicological testing in the United States, including for chemicals and drugs, was misleading scientists, and this worldwide issue persists today. Moreover, petroleum-based waste and heavy metals have been discovered in pesticide and plasticizer formulations. These contaminations have now reached all forms of life. Widespread exposure to chemical mixtures promotes health and environmental risks. We discovered that pesticides have never undergone long-term testing on mammals in their full commercial formulations by regulatory authorities or the pesticide industry; instead, only their declared active ingredients have been assessed, contrary to environmental law recommendations. The ingredients of these formulations are not fully disclosed, yet the formulations are in general at least 1000 times more toxic at low environmentally relevant doses than the active ingredients alone under conditions of long-term exposure. A similar lack of comprehensive toxicological evaluation applies to plasticizers. Their regulatory authorisations might have been obtained by incomplete, misleading and potentially false input data. This has profound implications not only for scientific knowledge, but also for public and environmental health. We propose pragmatically a paradigm shift in regulation: 1/to lower the ADI of polluting substances by at least a factor of 100 for already authorized products; 2/for new compounds, the obligation to test the full pesticide formulations in vivo chronically at environmentally relevant levels. This is necessary because pesticides are synthesized from petroleum, which can contain heavy metals. Moreover, formulated pesticides can contain plasticizers. The declared active substance, as an isolated compound of this mixture chosen by the company, will not have to be tested by itself alone. Compensation could be organized for pesticide use reduction, this will save health and environmental degradation; 3/the complete toxicological raw data for individual animals should be published on the Internet, including the precise protocols by which they were obtained, and they must be accessible for the scientific community, including students. There is no reason to keep these data secret. Implementing these changes would also support the advancement of agroecological alternatives.
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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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.041 | 0.060 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".