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Record W4389519156 · doi:10.1016/j.envpol.2023.123110

Spatial distribution of selenium-mercury in Arctic seabirds

2023· article· en· W4389519156 on OpenAlexaff
Marta Cruz‐Flores, Jérémy Lemaire, Maud Brault‐Favrou, Signe Christensen‐Dalsgaard, Carine Churlaud, Sébastien Descamps, Kyle H. Elliott, Kjell Einar Erikstad, А. В. Ежов, Maria Gavrilo, David Grémillet, Gaël Guillou, Scott A. Hatch, Nicholas Per Huffeldt, Alexander S. Kitaysky, Yann Kolbeinsson, Yuri Krasnov, Magdalene Langset, Sarah Leclaire, Jannie Fries Linnebjerg, Erlend Lorentzen, Mark L. Mallory, Flemming Ravn Merkel, William A. Montevecchi, Anders Mosbech, Allison Patterson, Samuel Perret, Jennifer F. Provencher, Tone K. Reiertsen, Heather M. Renner, Hallvard Strøm, Akinori Takahashi, Jean-Baptiste Thiébot, Þorkell Lindberg Þórarinsson, Alexis Will, Paco Bustamante, Jérôme Fort

Bibliographic record

VenueEnvironmental Pollution · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsEnvironment and Climate Change CanadaAcadia UniversityMemorial University of NewfoundlandMcGill University
FundersEuropean Regional Development FundInstitut Polaire Français Paul Emile VictorAgence Nationale de la Recherche
KeywordsArcticMercury (programming language)SeabirdEcologyTrophic levelδ15NSubarctic climateSpatial distributionEnvironmental scienceEnvironmental chemistryOceanographyStable isotope ratioChemistryδ13CBiologyGeographyGeologyPredation

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.089
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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 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

Citations18
Published2023
Admission routes1
Has abstractno

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