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Record W2004880138 · doi:10.14430/arctic4439

How Arctic Marine Birds Help Researchers Study a Changing North

2014· article· en· W2004880138 on OpenAlexaffvenueabout
Jennifer F. Provencher

Bibliographic record

VenueARCTIC · 2014
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsCarleton University
Fundersnot available
KeywordsArcticThe arcticGeographyOceanographyFisheryPhysical geographyBiologyGeology

Abstract

fetched live from OpenAlex

T he Arctic region is currently undergoing environmental change at an unprecedented rate (IPCC, 2007).Changing climatic conditions, a growing tourism industry, increasing levels of development and the associated marine shipping, and a growing human population are a few of the accumulating challenges the circumpolar Arctic is now facing (ACIA, 2004).The Canadian Arctic is no exception.Recent warming trends in Arctic Canada have led to reduced summer sea ice extent, as well as changes in snow line elevation and snowmelt (Wang and Overland, 2012;Miller et al., 2013).With the development of the tourism industry and the natural resource sector in northern Canada, ship traffic is predicted to increase, particularly around Baffin Island and the Northwest Passage (Smith and Stephenson, 2013;Dawson et al., 2014).In a time of rapid change, studies that examine how changes are affecting both the people and the environment are needed to develop evidence-based management and adaptation strategies (Armitage et al., 2011;Bring and Destouni, 2014).With more than 36 000 islands and 162 000 km of coastline, the marine environment represents a large component of the Canadian Arctic.Although the magnitude of the northern marine environment makes it challenging to conduct research there, access can be enhanced through the involvement and participation of the many communities that are widely distributed throughout the region.An additional approach when working in this geographically large and diverse region is to examine "indicator species": those that, in addition to being the focus of specific questions, also support a wider array of research objectives.As one example, marine birds are recognized as important global sentinels in marine ecology (Piatt et al., 2007), as well as in northern environments (Karnovsky et al., 2008).By definition, marine birds spend most of the year at sea, typically dispersed across vast tracts of ocean, but each summer they must return to land to breed, often in large nesting colonies (Gaston, 2004).This annual pattern allows research programs to establish protocols that are repeatable from year to year and to support research questions that benefit from long-term data sets (Gaston et al., 2009).Given the number of birds nesting at colonies, seabirds also offer larger sample sizes than many other wildlife species studied: researchers can often monitor tens of thousands of individuals at one location (Piatt et al., 2007).It is important that marine birds are also among the few marine indicator species that regularly visit the terrestrial environment, Jennifer Provencher is the 2014 recipient of the Jennifer

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.059
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0120.016
Scholarly communication0.0120.012
Open science0.0020.007
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0100.003

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 head0.089
GPT teacher head0.389
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2014
Admission routes3
Has abstractyes

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