Blue jays (Cyanocitta cristata) do not spontaneously eavesdrop on red squirrel (Tamiasciurus hudsonicus) squeals to locate food
Bibliographic record
Abstract
Organisms often eavesdrop on the cues and signals produced by other species to obtain information about their environment. Blue jays have dietary overlap with red squirrels, and learn to associate novel stimuli with food rewards in an experimental setting. Red squirrels produce “squeals” when contesting food resources with conspecifics. We tested whether blue jays eavesdrop on red squirrels by playing back red squirrel squeals, red squirrel rattles, white noise, and chick-a-dee calls to blue jays in Winnipeg, Manitoba. Additionally we examined the response of passerine birds in general to the playbacks, and attempted to condition free-living blue jays to respond to the playback of the squeal treatments. Results of the playbacks suggested that neither blue jays nor other passerines eavesdrop on vocalizations emitted in the context of red squirrel disputes over food. Conditioning trials did not produce any conditioned responses from blue jays; however, the limited number of trials performed does not constitute a robust test of the possible acquisition of a classically-conditioned response. Blue jays may also refrain from eavesdropping on red squirrel squeals as they are not reliable indicators of food resources, or because in an urban environment, blue jays readily learn the locations of bird feeders or other reliable food sources without eavesdropping on red squirrels.
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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) | 0.002 | 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".