Investigating the use of odour and colour foraging cues by rosy-faced lovebirds ( <i>Agapornis roseicollis</i> ) using deep-learning based behavioural analysis
Bibliographic record
Abstract
Abstract Olfaction and vision can play important roles in optimizing foraging decisions of birds, enabling them to maximize their net rate of energy intake while searching for, handling, and consuming food. Parrots have been used extensively in avian cognition research, and some species use olfactory cues to find food. Here we pioneered machine learning analysis and pose-estimation with convolutional neural networks (CNNs) to elucidate the relative importance of visual and olfactory cues for informing foraging decisions in the rosy-faced lovebird ( Agapornis roseicollis ) as a non-typical model species. In a binary choice experiment, we used markerless body pose tracking to analyse bird response behaviours. Rosy-faced lovebirds quickly learnt to discriminate the feeder provisioned with food by forming an association with visual (red/green papers) but not olfactory (banana/almond odour) cues. When visual cues indicated the provisioned and empty feeders, feeder choice was more successful, choice latency shorter, and interest in the empty feeder significantly lower. This demonstrates that visual cues alone are sufficient to inform lovebird foraging decisions without needing to use olfactory cues, suggesting that selection has not driven olfactory-based foraging in lovebird evolution.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".