Data from: The ability of North Island robins to discriminate between humans is related to their behavioural type
Bibliographic record
Abstract
Animals are able to learn to identify persistent threats to themselves and their offspring. For example, birds are able to quickly learn to discriminate between humans that have previously threatened their nests from humans with whom they have had no prior experience. However, no study has yet examined whether a bird's ability to discriminate between humans is related to the bird's underlying behavioural type. In this study, we examined whether there were differences among North Island (NI) robins (Petroica longipes), based on their underlying behavioural type, in their abilities to discriminate between familiar and novel human observers. Using a simple feeding experiment, we timed how long it took birds to attack a food item placed next to an observer on each of 7 days. On the eighth day, a different observer timed the birds. We found that birds could be split into two behaviour types based on their attack behaviour: fast attackers (latencies <20 sec) and slow attackers (latencies >20 secs). Interestingly, the fast birds did not increase their attack latency in response to the novel observer whereas the slow attackers did. This result, for the first time, demonstrates that a bird's ability to discriminate between humans can vary among birds based on their behavioural type.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.024 |
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".