PFAS in Paper and Board for Food Contact
Bibliographic record
Abstract
Poly- and perfluorinated alkyl substances (PFAS) are used in paper and board food contact materials (FCMs) and they have been found to be highly persistent, bioaccumulative and toxic. The purpose of the Nordic workshop and of this report is to:* create an overview of the use of PFAS in FCMs of paper and board and of the toxicity and migration into food of the various substances* provide an overview of whether appropriate risk assessments for fluorinated substances exist as a basis for specific regulations or recommendations* provide an overview of whether analytical methods suitable for analysing and regulating the substances are available* discuss the possibility and structure of national regulations or Nordic recommendations for PFAS in FCMs of paper and board. Risk management to reduce the total content of organically bound fluorine in paper and board FCMs is supported. The given report is published in continuation of a Nordic workshop on January 28th -29th 2015 on poly- and perfluorinated substances (PFAS) in food contact materials. Representatives from EU MS countries, US FDA, Canada and China, as well as manufacturers, retailers, compliance testing laboratories and academia were present in the workshop and contributed to the report.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.032 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".