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Record W7128608518 · doi:10.26108/hryy-wx58

Hotspots in cold seas: The composition, distribution, and abundance of marine birds in the North American Arctic

2014· article· en· W7128608518 on OpenAlexaboutno aff
Sarah N. P. Wong, Carina Gjerdrum, Ken H. Morgan, Mark L. Mallory

Bibliographic record

VenueAcadiaU-DEV · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsArcticAbundance (ecology)ForagingArctic ecologySeabirdSea iceThe arcticArctic ice pack

Abstract

fetched live from OpenAlex

The distribution and thickness of sea ice in the Arctic is changing rapidly, resulting in changes to Arctic marine ecosystems. Seabirds are widely regarded as indicators of marine environmental change, and understanding their distribution patterns can serve as a tool to monitor and elucidate biological changes in the Arctic seas. We examined the at-sea distribution of seabirds in the North American Arctic in July and August, 2007–2012, and marine areas of high density were identified based on bird densities for four foraging guilds. Short-tailed shearwaters (Puffinus tenuirostris) were the most abundant species observed. Northern fulmars (Fulmarus glacialis), thick-billed murres (Uria lomvia), and dovekies (Alle alle) were also sighted in large numbers. Few birds were sighted between Dolphin and Union Strait and King William Island. Areas of high density over multiple years were found throughout the entire western portion of the study area (Bering Sea, Bering Strait, and Chukchi Sea), Lancaster Sound, Baffin Bay, Davis Strait, and the low Arctic waters off Newfoundland. These waters are characterized by high primary productivity. This study is the first to document the marine distribution of seabirds across the entire North American Arctic within the same time period, providing a critical baseline for monitoring the distribution and abundance of Arctic seabirds in a changing Arctic seascape.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.602

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 studies0.0000.001
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 head0.005
GPT teacher head0.214
Teacher spread0.209 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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