Black-capped Chickadees (<i>Poecile atricapillus</i>) can identify individual females by their <i>fee-bee</i> songs
Bibliographic record
Abstract
Abstract Individual recognition is a social behavior that occurs in many bird species. A bird’s ability to discriminate among familiar and unfamiliar conspecifics is critical to avoid wasting resources such as time and energy during social interactions. Black-capped Chickadees (Poecile atricapillus) are able to discriminate individual female and male chick-a-dee calls, potentially male and female tseet calls, and male fee-bee songs. In the current study, we used an operant discrimination go/no-go paradigm to determine whether female and male chickadees could discriminate between fee-bee songs produced by individual female chickadees as well as test which song component(s) enable this discrimination. Birds trained on natural categories—the songs of different females—learned to respond to rewarded stimuli more quickly than birds trained on random groupings of female songs and were able to transfer this learning to new songs from the same categories. Chickadees were also able to generalize their responding when exposed to the bee note of the fee-bee song of rewarded individuals; they did not generalize to fee notes. Our results provide evidence that Black-capped Chickadees can use female-produced fee-bee songs for individual recognition. However, the acoustic features underlying individual recognition require further investigation.
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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 | 0.001 |
| 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".