AGRICOH, A NEWYLY FORMED CONSORTIUM OF AGRICULTURAL COHORTS
Bibliographic record
Abstract
Background and Aims: Chronic exposure to pesticides has endocrine, immunologic and neurologic disrupting properties and other toxic effects believed to be associated with certain cancers, unfavorable reproductive outcomes, neurologic disorders and other health outcomes. Farming and other agricultural jobs are among the occupations with the highest exposures to pesticides. AGRICOH is a consortium of agricultural cohort studies that offers the opportunity to investigate the role of pesticides, and other exposures, in the etiology of several health outcomes. This poster presentation provides a description of AGRICOH to disseminate its formation and future research plans. Methods: Characteristics of the cohorts integrating AGRICOH, data harmonization plans and research concepts to be studied will be illustrated in the poster presentation including the construction of pesticide crop exposure matrices. Results: AGRICOH is a consortium of 22 agricultural cohort studies initiated by the US National Cancer Institute (NCI) and coordinated by the International Agency for Research on Cancer (IARC) since October 2010. The consortium includes cohorts from 9 countries: South Africa (1), Canada (3), Costa Rica (2), USA (6), Korea (1), New Zealand (2), Denmark (1), France (3) and Norway (3). AGRICOH aspires to promote and sustain collaboration and pooling of data to investigate the association between a wide range of agricultural exposures and a wide range of health outcomes, with a particular focus on associations that cannot easily be addressed in individual studies because of rare exposures (e.g. use of infrequently applied pesticides) or relatively rare outcomes (e.g. certain types of cancer, neurologic and auto-immune diseases). Conclusions: AGRICOH represents a great resource for studying cancer, respiratory, neurologic, and auto-immune diseases as well as reproductive and allergic disorders, injuries and overall mortality in association with a wide array of exposures, prominent among these the application of pesticides.
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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 | 0.058 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".