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Record W1594602530

Aerial surveys of Greater White-fronted Geese Anser albifrons frontalis and other waterfowl in the Rasmussen Lowlands of the Central Canadian Arctic.

2013· article· en· W1594602530 on OpenAlexfundaboutno aff
James E. Hines, Mayson Kay, Meghan Wiebe

Bibliographic record

VenueWildfowl (Wildfowl & Wetlands Trust) · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
FundersNatural Resources Canada
KeywordsGeographyWaterfowlWhite (mutation)ArcticThe arcticFisheryHabitatEcologyOceanographyGeologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Helicopter surveys w e re carried out in June 1994-95 to determine the num bers and distribution of Greater White-fronted Geese Anser albifrons frontalis and other waterfowl in the Rasm ussen Lowlands of the Central Canadian Arctic.The results provide information needed for the m an agement of Greater White-fronted Geese as well as for evaluating the biological importance of the R asm ussen Lowlands as a potential 'p ro tected area'.Estimated num bers of waterfowl in the 6, 265km 2 survey area were 42, 041 Lesser Snow Geese Anser caerulescens caerulescens, 23, 516 Greater White-fronted Geese, 13, 690 King Eiders Somateria spectabilis, 6, 412 Canada Geese Branta canadensis hutchinsii, 5, 427 Long-tailed Ducks Clangula hyemaIis and 3, 822 Tundra Sw an s Cygnus columbianus.Sm aller num bers of several other species of aquatic and terrestrial birds were observed and minimum population estimates are reported for those species as well.Num bers of Lesser Snow, Greater White-fronted and Canada Geese have increased substantially in the Rasm ussen Lowlands since the m id -1970s, but King Eiders and Long tailed Ducks have declined markedly.The results support previous find ings that the R asm ussen Lowlands is a critical breeding and sum m er area for Greater White-fronted Geese and other arctic-nesting water fowl, and further strengthen the recommendations that this site should be protected.

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 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 score0.136
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.179
Teacher spread0.171 · 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

Citations3
Published2013
Admission routes2
Has abstractyes

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