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Current state of knowledge on biological effects from contaminants on arctic wildlife and fish

2019· article· en· W2967738632 on OpenAlexafffund
Runé Dietz, Robert J. Letcher, Jean‐Pierre Desforges, Igor Eulaers, Christian Sonne, Simon Wilson, Emilie Andersen-Ranberg, Niladri Basu, Benjamin D. Barst, Jan Ove Bustnes, Jenny Bytingsvik, Tomasz Maciej Ciesielski, Paul E. Drevnick, Geir Wing Gabrielsen, Ane Haarr, Ketil Hylland, Bjørn Munro Jenssen, Milton Levin, Melissa A. McKinney, Rasmus Dyrmose Nørregaard, Kathrine Eggers Pedersen, Jennifer F. Provencher, Bjarne Styrishave, Sabrina Tartu, Jon Aars, Joshua T. Ackerman, Aqqalu Rosing‐Asvid, Rob Barrett, Anders Bignert, Erik W. Born, Marsha Branigan, Birgit M. Braune, Colleen E. Bryan, Maria Dam, Collin A. Eagles‐Smith, Marlene S. Evans, Thomas J. Evans, Aaron T. Fisk, Mary Gamberg, Kim Gustavson, C. Alex Hartman, Björn Helander, Mark P. Herzog, Paul F. Hoekstra, Magali Houde, Katrin S. Hoydal, Allyson K. Jackson, John R. Kucklick, Elisabeth Lie, Lisa L. Loseto, Mark L. Mallory, Cecilie Miljeteig, Anders Mosbech, Derek C. G. Muir, Sanna Túni Nielsen, Elizabeth Peacock, Sara Pedro, Sarah H. Peterson, Anuschka Polder, Frank F. Rigét, Pat Roach, Halvor Saunes, Mikkel‐Holger S. Sinding, Janneche Utne Skaare, Jens Søndergaard, Garry B. Stenson, Gary A. Stern, Gabriele Treu, Stacy S. Schuur, Gísli A. Víkingsson

Bibliographic record

VenueThe Science of The Total Environment · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsAboriginal Affairs Northern Dev CanadaUniversity of ManitobaUniversity of WindsorAcadia UniversityShell (Canada)Government of Northwest TerritoriesGovernment of NunavutAlberta Environment and Protected AreasMcGill UniversityEnvironment and Climate Change CanadaFisheries and Oceans CanadaCarleton University
FundersHealth CanadaNatural Sciences and Engineering Research Council of CanadaU.S. Geological SurveyNational Institute of Standards and TechnologyEnvironment and Climate Change CanadaIndigenous and Northern Affairs CanadaMiljøstyrelsenAarhus Universitet
KeywordsPopulationSentinel speciesArcticWildlifeBiologyBiotaEcologyZoologyEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.245
Teacher spread0.229 · 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
GenreReview

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

Citations268
Published2019
Admission routes2
Has abstractyes

Explore more

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