A bird that changes colour without moulting: how the <i>wîskicâhk</i> (Canada Jay, <i>Perisoreus canadensis</i>) tricked the taxonomists
Bibliographic record
Abstract
Avian and mammalian colours are thought to be constant in life and in museum specimens, but several early 20th-century taxonomists singled out the Canada Jay (Perisoreus canadensis (Linnaeus, 1766)) as having unstable feather pigments and warned against using old museum specimens for taxonomic purposes. One such error was Brisson’s (1760) original naming of the species as “the Brown Jay of Canada”. Another was Ridgway’s (1899) naming of the “Gray Jay” as a new subspecies, Perisoreus (canadensis) griseus, through inappropriate comparison of fresh grey specimens with old brown ones. We discovered that browning of initially grey plumage also occurs between annual moults in living individuals of the Canada Jay’s Pacific morphotype. We documented this change using photographs and re-sightings of colour-banded individuals and through spectral analysis of year-old (brown) and incoming (grey) rectrices collected from the same moulting individuals. To assess the distribution of this colour change, we compared September vs. May eBird photographs from across North America. We showed that seasonal colour change is normal in the Pacific morphotype but rare in the two other morphotypes. Collectively, these data have important implications for the taxonomy of the Canada Jay and are a cautionary tale for taxonomists studying animal colouration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".