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Record W2897340491 · doi:10.1139/er-2018-0067

Identifying key marine habitat sites for seabirds and sea ducks in the Canadian Arctic

2018· article· en· W2897340491 on OpenAlexafffundvenueabout
Mark L. Mallory, Anthony J. Gaston, Jennifer F. Provencher, Sarah N. P. Wong, Christine Anderson, Kyle H. Elliott, H. Grant Gilchrist, Michael Janssen, Thomas Lazarus, Allison Patterson, Lisa Pirie‐Dominix, Nora C. Spencer

Bibliographic record

VenueEnvironmental Reviews · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsEnvironment and Climate Change CanadaAcadia UniversityMcGill UniversityCanadian Institute for Advanced Research
FundersIndigenous and Northern Affairs CanadaEnvironment and Climate Change CanadaMitacsArcticNetCanada Research ChairsPolar Knowledge CanadaFulbright CanadaAcadia University
KeywordsHabitatArcticMarine protected areaGeographyClimate changeMarine habitatsEcologyPopulationFisheryMarine conservationOceanographyBiology

Abstract

fetched live from OpenAlex

The Canadian Arctic hosts millions of marine birds annually, many of which aggregate in large numbers at well-defined sites at predictable times of the year. Marine habitats in this region will be under increasing threats from anthropogenic activities, largely facilitated by climate change and long-term trends of reduced sea ice extent and thickness. In this review, we update previous efforts to delineate the most important habitats for marine birds in Arctic Canada, using the most current population estimates for Canada, as well as recent information from shipboard surveys and telemetry studies. We identify 349 160 km 2 of key habitat, more than doubling earlier suggestions for key habitat extent. As of 2018, 1% of these habitats fall within the boundaries of legislated protected areas. New marine conservation areas currently being finalized in the Canadian Arctic will only increase the proportion protected to 13%.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score1.000

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)0.0020.001

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 head0.040
GPT teacher head0.273
Teacher spread0.233 · 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; both teacher heads agree on what is shown here.

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

Citations32
Published2018
Admission routes4
Has abstractyes

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