The impact and toxicity of glyphosate and glyphosate-based herbicides on health and immunity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
, has continued to increase since 1974; glyphosate, as well as its primary metabolite aminomethylphosphonic acid, is measured in soils, water, plants, animals and food. In humans, glyphosate is detected in blood and urine, especially in exposed workers, and is excreted within a few days. It has long been regarded as harmless in animals, but growing literature has reported health risks associated with glyphosate and glyphosate-based herbicides. In 2017, the International Agency for Research on Cancer (IARC) classified glyphosate as "probably carcinogenic" in humans. However, other national agencies did not tighten their glyphosate restrictions and even prolonged authorizations of its use. There are also discrepancies between countries' authorized levels, demonstrating an absence of a clear consensus on glyphosate to date. This review details the effects of glyphosate and glyphosate-based herbicides on fish and mammal health, focusing on the immune system. Increasing evidence shows that glyphosate and glyphosate-based herbicides exhibit cytotoxic and genotoxic effects, increase oxidative stress, disrupt the estrogen pathway, impair some cerebral functions, and allegedly correlate with some cancers. Glyphosate effects on the immune system appear to alter the complement cascade, phagocytic function, and lymphocyte responses, and increase the production of pro-inflammatory cytokines in fish. In mammals, including humans, glyphosate mainly has cytotoxic and genotoxic effects, causes inflammation, and affects lymphocyte functions and the interactions between microorganisms and the immune system. Importantly, even as many outcomes are still being debated, evidence points to a need for more studies to better decipher the risks from glyphosate and better regulation of its global utilization.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it