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Record W4318586471 · doi:10.32920/21977372

A Long Way From Home—Industrial Chemicals in the Arctic That Really Should Not Be There

2023· preprint· en· W4318586471 on OpenAlexaffabout
Roxana Suehring, Miriam L. Diamond, Martin Scheringer, Liisa M. Jantunen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsUniversity of TorontoEnvironment and Climate Change Canada
Fundersnot available
KeywordsArcticThe arcticBusinessEnvironmental scienceEnvironmental protectionOceanographyGeology

Abstract

fetched live from OpenAlex

It is hard to believe, but some of the chemicals in our couches, computers, and even phones can travel all the way to the Arctic. How is that possible? That is exactly what we were asking when we found chemicals that are used in everyday items—like computers, phones, and couches—in the Canadian Arctic. In this article, we will tell you about our research into these chemicals in the Canadian Arctic and what we found out about their abilities to “fly” and “swim” north to the Arctic. We will also share our ideas on how we can keep animals and people in the Arctic—and around the world—safe from some of these chemicals.

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)0.002
metaresearch head score (Gemma)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.716
Threshold uncertainty score0.572

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0180.011
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0170.003

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 head0.270
GPT teacher head0.384
Teacher spread0.114 · 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
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

Citations0
Published2023
Admission routes2
Has abstractyes

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