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Record W325183857 · doi:10.6027/tn2013-542

Per- and polyfluorinated substances in the Nordic Countries

2013· report· en· W325183857 on OpenAlexaff
Stefan Posner, Sandra Roos, Pia Brunn Poulsen, Hrönn Ólína Jörundsdóttir, Helga Gunnlaugsdóttir, Xenia Trier, Allan Astrup Jensen, Athanasios A. Katsogiannis, Dorte Herzke, Eva Cecilie Bonefeld‐Jørgensen, Christina Jönsson, Gitte Alsing Pedersen, Mandana Ghisari, Sophie Jensen

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsNordic Life Science Pipeline (Canada)
Fundersnot available
KeywordsEnvironmental science

Abstract

fetched live from OpenAlex

This Tema Nord report presents a study based on open information and custom market research to review the most common perfluorinated substances (PFC) with less focus on PFOS and PFOA.The study includes three major parts: 1) Identification of relevant per-and polyfluorinated substances and their use in various industrial sectors in the Nordic market by interviews with major players and database information. 2) Emissions to and occurence in the Nordic environment of the substances described in 1). 3) A summary of knowledge of the toxic effects on humans and the environment of substances prioritized in 2). There is a lack of physical chemical data, analystical reference substances, human and environmental occurrence and toxicology data, as well as market information regarding PFCs other than PFOA and PFOS and the current legislation cannot enforce disclosure of specific PFC substance information.

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)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.085
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.006
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.301
Teacher spread0.272 · 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
GenreOther

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

Citations28
Published2013
Admission routes1
Has abstractyes

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