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Record W3014313718 · doi:10.3996/092019-jfwm-082

Exploratory Surveys of Migratory Birds Breeding in the Western and Central Canadian Arctic 2005–2011

2020· article· en· W3014313718 on OpenAlexaboutno aff
Pamela R. Garrettson, Kammie L. Kruse, Timothy J. Moser, Deborah J. Groves

Bibliographic record

VenueJournal of Fish and Wildlife Management · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyWaterfowlWildlifeTundraArcticHabitatFisheryWildlife refugeEcologySubarctic climateSeabirdBreeding pairAbundance (ecology)Aerial surveyBiologyPredationArchaeologyPopulation

Abstract

fetched live from OpenAlex

Abstract The Canadian Arctic and subarctic are the primary breeding areas of many species of North American water and land birds. Because of the remote location and the logistical difficulties of working there, wildlife biologists have not systematically surveyed most important areas for wildlife, nor have they surveyed these areas very frequently. During the summers of 2005–2011, various Joint Ventures, and U.S., Canadian, and state wildlife agencies and other partners funded exploratory fixed-wing aircraft surveys of migratory birds (excluding passerines and shorebirds) in important habitats in Canada's western and central Arctic. Our objectives were to provide access to the complete survey dataset (all bird and mammal observations and associated location data) and summarize information on several species. Thus, we produced maps of average relative density and estimates of abundance in the survey area for cackling geese Branta hutchinsii, greater white-fronted geese Anser albifrons, tundra swans Cygnus columbianus, king eiders Somateria spectabilis, long-tailed ducks Clangula hyemalis, white-winged Melanitta fusca and surf Melanitta perspicillatas scoters, and yellow-billed Gavia adamsii, red-throated Gavia stellata, and Pacific Gavia pacifica loons. We reviewed previous survey efforts in the area and, where possible, compared them with our results.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.211
Teacher spread0.190 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2020
Admission routes1
Has abstractyes

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