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Record W2430761673 · doi:10.1080/00063650309461315

Ornithology and bird conservation in North America – a Canadian perspective

2003· article· en· W2430761673 on OpenAlexaboutno aff
Fred Cooke

Bibliographic record

VenueBird Study · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsOrnithologyGeographyIndigenousWildlifePopulationConservation biologyEcologySouthern HemisphereBiologyDemographySociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.023
GPT teacher head0.247
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2003
Admission routes1
Has abstractyes

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