Deformed frogs and environmental retinoids
Bibliographic record
Abstract
Since the early 1990s, a substantial number of deformed frogs have been observed in North America, particularly in the upper Midwest and Canada. Attempts to understand the etiology of the deformed frog problem have met with limited success to date with nearly as many proposed explanations as research groups working on the problem. Models for the mechanism underlying the development of deformed frogs include parasite/predation, ultraviolet radiation, and chemical exposure. Each model has its strengths and weaknesses. Despite contentious debate among researchers, there is an overall consensus that the increasing prevalence of deformed frogs is the result of a water-borne contaminant that has recently appeared, or reached a critical concentration. Our detailed analysis of malformed frogs collected in Minnesota ponds and lakes suggested that limb patterning was being modified by the disruption of a retinoid-sensitive developmental signaling pathway. Accordingly, we focused in the identification and characterization of bioactive retinoids from lake water and showed that retinoid treatment of frog embryos at sensitive times of development could recapitulate the full spectrum of limb abnormalities observed in field specimens in the laboratory. These data have led to the conclusion that inappropriate modulation of retinoid signaling by environmental contaminants is the mechanism underlying the increased incidence of frog malformations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".