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Record W4213044125 · doi:10.1201/9781003265177-7

Role of Persistent Organic Pollutants and Mercury in the Arctic Environment and Indirect Impact on Climate Change

2022· book-chapter· en· W4213044125 on OpenAlexaboutno aff
Anoop Kumar Tiwari, Tara Megan da Lima Leitao

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsPollutantEnvironmental scienceArcticMercury (programming language)Climate changeThe arcticEnvironmental chemistryClimatologyOceanographyChemistryGeologyEcologyComputer scienceBiology

Abstract

fetched live from OpenAlex

The north pole comprises landmasses above the 60°N latitude contributed by countries such as Russia, United States of America, Alaska, Norwich, Sweden, Finland, Greenland, Iceland, Canada and Norway. Resident indigenous peoples are enduring extreme conditions daily and are dependent on local produce for survival. Pollutants may be produced at the poles or travel over considerable distances through the atmosphere and oceans and get deposited in the Polar Regions. The prevalence of POPs in environmental matrices depends on their octanol: water partition coefficients, fugacity ratios, enantiomer fraction and photochemical oxidation, amongst other in situ factors. Active sampling is a convenient means by which air can be sampled, requiring a power source and constant maintenance. The GC/MS analyses for ice core trapped POPs were modified to trimethylsilyl esters and esters with bis (trimethylsilyl) trifluoroacetamide to retain 75% recovery in a lipid fraction prior to analyses.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.024
GPT teacher head0.233
Teacher spread0.209 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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