Improving the language of migratory bird science in North America
Bibliographic record
Abstract
Abstract Several long-accepted terms are widely misused in ornithology and have led to a misperception of important concepts in the ecology of Nearctic–Neotropical migratory birds. The term “North America” (and its ancillary terms “North American species,” “North American habitats,” etc.) is widely used to refer to the United States and Canada, when in fact it should include all of the continent from the Arctic through Panama. In a similar vein, the terms “wintering” and “over-wintering” (whether used to describe the status of individual birds or species, or as a modifier for terms like habitats, ecology, or behavior), “spring migration” and “fall migration” are inappropriate for Nearctic–Neotropical migrants because they explicitly reference conditions in the temperate zone of the continent, even as most such species spend the majority of their annual cycle elsewhere, where these terms are inaccurate and unhelpful. We discuss the pitfalls of using these terms and suggest several alternatives and replacements. In particular, we urge more precision in the use of the term “North America”; for Nearctic–Neotropical migratory species (especially long-distance migrants), we suggest retiring the terms “wintering” and “over-wintering” in favor of “nonbreeding”; and for the same group of species we suggest retiring the terms “spring migration” and “fall migration” in favor of “pre-breeding,” “post-breeding,” or “post-natal” migration.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".