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Record W4401206151 · doi:10.1139/as-2024-0037

A History of Monitoring Marine Birds at Sea in Eastern and Arctic Canada

2024· article· en· W4401206151 on OpenAlexaffvenueabout
Carina Gjerdrum, David A. Fifield, François V. Bolduc, Sarah N. P. Wong, Matthieu Beaumont, Mark L. Mallory

Bibliographic record

VenueArctic Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsAcadia UniversityEnvironment and Climate Change Canada
Fundersnot available
KeywordsArcticOceanographyGeographyThe arcticFisheryEnvironmental scienceBiologyGeology

Abstract

fetched live from OpenAlex

Systematic surveys of marine birds from ships were first conducted by the Canadian Wildlife Service (CWS) in Atlantic Canada in 1965, and then expanded to the Canadian Arctic in 1969 under PIROP (Programme intégré de recherches sur les oiseaux pélagiques). PIROP surveys ended in 1992, then resumed in 2006 under the Eastern Canada Seabirds at Sea (ECSAS) program with an updated survey protocol. Surveys under both monitoring programs were conducted from a variety of ship types engaged in scientific, transport, and supply activities, totalling over 120,000 km within sub-Arctic and Arctic Canada waters and over a million marine birds observed, primarily northern fulmar (Fulmarus glacialis), black-legged kittiwake (Rissa tridactyla), thick-billed murre (Uria lomvia), and dovekie (Alle alle). The data collected inform offshore ecological inquiries, environmental impact reviews, mortality estimates from accidental oil releases, and define areas in need of protection. Although surveys were designed to quantify seabird distribution within the waters of eastern Canada, the data also include sightings of non-avian taxa that are made publicly available. Long-term and large-scale monitoring programs will remain essential for assessing the status and health of Canada’s marine birds, including surveys that take place at sea where these species spend most of their time.

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 score0.099
Threshold uncertainty score0.744

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)0.0010.000

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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
Published2024
Admission routes3
Has abstractyes

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