Concerted conservation actions to support chimpanzee cultures
Bibliographic record
Abstract
Chimpanzees were among the first animals recognized to have culture, and our understanding of the breadth of their cultural repertoire has grown significantly since the 1960s. Throughout their range, chimpanzee populations have come under increasing pressure, with their endangered status necessitating immediate and long-term conservation interventions. Recognizing the importance of diverse behavioural repertoires for chimpanzees' survival, there has been a recent focus of conservation efforts on preserving their culturally transmitted behaviours and the environments in which they are exhibited. This article evaluates the practicality of developing conservation measures focused on chimpanzee culture. We highlight innovative conservation strategies aimed at integrating chimpanzee cultural behaviours into conservation policies. We review synergistic conservation initiatives led by the International Union for Conservation of Nature, the UN Convention on the Conservation of Migratory Species of Wild Animals and other international and local groups that share the goal of preserving chimpanzee populations and their cultural diversity. We underline how successful conservation implementation requires engagement and collaboration with a diverse group of interested or affected people. Finally, we provide recommendations aimed at guiding future efforts to incorporate animal cultures into conservation strategies.This article is part of the theme issue 'Animal culture: conservation in a changing world'.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".