The <scp>OpenFeeder</scp> : A flexible automated <scp>RFID</scp> feeder to measure interspecies and intraspecies differences in cognitive and behavioural performance in wild birds
Bibliographic record
Abstract
Abstract Understanding the ecology and evolution of personality and cognition requires the development of new tools to measure individual and species differences in behavioural and cognitive performances in wild populations. Furthermore, such tools should facilitate collection of large sample sizes, evaluate the repeatability of measured traits and allow direct comparison of species performances across a variety of behavioural tasks. Here we present a RFID‐based feeder (OpenFeeder) designed to run visual cognitive tasks in wild animals. We illustrate the flexibility of the tool showing performances of three wild passerine species ( Parus major , Cyanistes caeruleus and Poecile palustris ) in an associative learning task. We recorded performances of a large number of individuals (>300) in the wild and showed both interspecific and intraspecific differences in associative learning. We also found moderate to high repeatability in individual differences in associative learning in each species. We show that the OpenFeeder is a flexible tool to record performance in multiple cognitive and behavioural tasks in free‐ranging animals across a variety of passerine species. The design, firmware and software are open source to facilitate use in a wide variety of species and thus allow continuous improvement of the system and development of new behavioural and cognitive tasks. In doing so, we hope that this tool will be used by a large community of cognitive ecologists and comparative psychologists for both within and across species studies. Furthermore, our system should facilitate replication of results across populations along large‐scale environmental gradients to improve our understanding of the role ecology plays in the evolution of cognitive traits.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".