One Health Approach to Globalizing, Accelerating, and Focusing Amphibian and Reptile Disease Research—Reflections and Opinions from the First Global Amphibian and Reptile Disease Conference
Bibliographic record
Abstract
The world's reptiles and amphibians are experiencing dramatic and ongoing losses in biodiversity, changes that can have substantial effects on ecosystems and human health. In 2022, the first Global Amphibian and Reptile Disease Conference was held, using One Health as a guiding principle. The conference showcased knowledge on numerous reptile and amphibian pathogens from several standpoints, including epidemiology, host immune defenses, wild population effects, and mitigation. The conference also provided field experts the opportunity to discuss and identify the most urgent herpetofaunal disease research directions necessary to address current and future threats to reptile and amphibian biodiversity.
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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 | 0.045 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.031 | 0.052 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".