The complex effects of modern oncogenic environments on the fitness, evolution and conservation of wildlife species
Bibliographic record
Abstract
Growing evidence indicates that human activities are causing cancer rates to rise in both human and wildlife populations. This is due to the inability of ancestral anti-cancer defences to cope with modern environmental risks. The evolutionary mismatch between modern oncogenic risks and evolved cancer defences has far-reaching effects on various biological aspects at different timeframes, demanding a comprehensive study of the biology and evolutionary ecology of the affected species. Firstly, the increased activation of anti-cancer defences leads to excessive energy expenditure, affecting other biological functions and potentially causing health issues like autoimmune diseases. Secondly, tumorigenesis itself can impact important fitness-related parameters such as competitiveness, predator evasion, resistance to parasites, and dispersal capacity. Thirdly, rising cancer risks can influence the species' life-history traits, often favoring early reproduction to offset fitness costs associated with cancer. However, this strategy has its limits, and it may not ensure the sustainability of the species if cancer risks continue to rise. Lastly, some species may evolve additional anti-cancer defences, with uncertain consequences for their biology and future evolutionary path. In summary, we argue that the effects of increased exposure to cancer-causing substances on wildlife are complex, ranging from immediate responses to long-term evolutionary changes. Understanding these processes, especially in the context of conservation biology, is urgently needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".