How orangutans (Pongo pygmaeus) innovate for water.
Bibliographic record
Abstract
We report an observational field study that aimed to identify innovative processes in rehabilitant orangutans' (Pongo pygmaeus) water innovations on Kaja Island, Central Kalimantan, Indonesia. We tested for the basic model of innovating (make small changes to old behavior), 4 contributors (apply old behavior to new ends, accidents, independent working out, social cross-fertilization), development, and social rank. Focal observations of Kaja rehabilitants' behavior over 20 months yielded 18 probable innovations from among 44 water variants. We identified variants by function and behavioral grain, innovations by prevalence, and innovative processes by relations between innovations, other behaviors, and social encounters. Findings indicate innovating by small changes and some involvement of all 4 contributors; midrank orangutans were the most innovative; and rehabilitants' adolescent age profile, orphaning, and intense sociality probably enhanced innovativeness. Important complexities include: orangutan innovating may favor certain behavioral levels and narrowly defined similarities, and it may constitute a phase-like process involving a succession of changes and contributors. Discussion focuses on links with great ape cognition and parallels with innovating in humans and other nonhuman species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".