Approaches and methods to study wildlife cancer
Bibliographic record
Abstract
The last few years have seen a surge of interest from field ecologists and evolutionary biologists to study neoplasia and cancer in wildlife. This contributes to the One Health Approach, which investigates health issues at the intersection of people, wild and domestic animals, together with their changing environments. Nonetheless, the emerging field of wildlife cancer is currently constrained by methodological limitations in detecting cancer using non-invasive sampling. In addition, the suspected differential susceptibility and resistance of species to cancer often make the choice of a unique model species difficult for field biologists. Here, we provide an overview of the importance of pursuing the study of cancer in non-model organisms and we review the currently available methods to detect, measure and quantify cancer in the wild, as well as the methodological limitations to be overcome to develop novel approaches inspired by diagnostic techniques used in human medicine. The methodology we propose here will help understand and hopefully fight this major disease by generating general knowledge about cancer, variation in its rates, tumour-suppressor mechanisms across species as well as its link to life history and physiological characters. Moreover, this is expected to provide key information about cancer in wildlife, which is a top priority due to the accelerated anthropogenic change in the past decades that might favour cancer progression in wild populations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".