Ornithology and bird conservation in North America – a Canadian perspective
Bibliographic record
Abstract
Capsule A comparison between North American and European ornithology shows many differences between the two. While local knowledge was developed over millennia in Europe, in North America much was originally learned from the indigenous people. Knowing the food value of wild game was essential to survival and led to a strong tradition of wildlife management in North America. However, there was also systematic exploration of the local avifauna by museum-based collectors. This dual origin of ornithology is still detectable today. North American ornithology, particularly through the introduction of the Migratory Birds Convention Act of 1917, is strongly influenced by federal, state and provincial governments who have a statutory responsibility for the protection of wild birds. Because the USA and Canada, and more recently Mexico, are responsible for the administration of the Act, many initiatives in bird conservation involve international co-operation, starting initially with the North American Waterfowl Management Plan, but now extended to all species of birds. Many partnerships involving government, professional and amateur ornithologists (e.g. Bird Studies Canada) have resulted in monitoring of bird populations similar to that done by the BTO. However, there still seems to be a paucity of population studies and survey information in the mainstream ornithological journals in North America.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".