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Record W2179625534 · doi:10.1016/j.envpol.2015.11.006

Twenty years of monitoring of persistent organic pollutants in Greenland biota. A review

2015· review· en· W2179625534 on OpenAlexaff
Frank F. Rigét, Katrin Vorkamp, Rossana Bossi, Christian Sonne, Robert J. Letcher, Runé Dietz

Bibliographic record

VenueEnvironmental Pollution · 2015
Typereview
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsEnvironment and Climate Change CanadaCarleton University
FundersKommissionen for Videnskabelige Undersøgelser i GrønlandPinngortitaleriffikAarhus Universitet
KeywordsArcticBiotaEnvironmental monitoringEnvironmental scienceThe arcticPollutantPhysical geographyEnvironmental resource managementGeographyOceanographyEcologyEnvironmental engineeringGeologyBiology

Abstract

fetched live from OpenAlex

The Arctic Monitoring and Assessment Programme (AMAP) is a working group under the Arctic Council with the aim to monitor and assess temporal trends of contaminants in Arctic ecosystems. The Greenland AMAP Core programme was established to contribute to this effort. The Core programme includes three main components; routine monitoring, retrospective studies and new POP screening studies. The programme is based on an adaptive approach, which has led to changes throughout the years. An overview of the temporal trends during the last two to three decades is presently given together with selected examples of different characteristic trends of POPs. The results show how tissue banked samples and retrospective studies has helped in establishing time-series of compounds of emerging concern. Lastly, the statistical power of the Greenlandic time-series is discussed. The lesson learned is that trend monitoring improves with samples over time, and only pays off after decades of data are generated.

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)0.001
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 score0.005
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.032
GPT teacher head0.283
Teacher spread0.251 · 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

Citations107
Published2015
Admission routes1
Has abstractyes

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Same venueEnvironmental PollutionSame topicToxic Organic Pollutants ImpactFrench-language works237,207