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Record W4246269658 · doi:10.2903/sp.efsa.2020.en-1835

Outcome of the public consultation on the draft scientific report on the cumulative dietary risk characterisation of pesticides that have acute effects on the nervous system

2020· article· en· W4246269658 on OpenAlexfundno aff

Bibliographic record

VenueEFSA Supporting Publications · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural safety and regulations
Canadian institutionsnot available
FundersWageningen University and ResearchHealth CanadaU.S. Environmental Protection Agency
KeywordsRisk assessmentBusinessEnvironmental healthFood safetyPolitical sciencePublic relationsMedicineAccountingManagementPathologyEconomics

Abstract

fetched live from OpenAlex

The European Food Safety Authority (EFSA) carried out a public consultation to receive input from interested parties on its draft scientific report on the cumulative dietary risk characterisation of pesticides that have acute effects on the nervous system. The document describes the process and the outcome of a risk assessment and an uncertainty analysis regarding the cumulative effects of pesticide residues on acetylcholinesterase and the motor division of the nervous system. The web‐based public consultation took place from 17 September to 15 November 2019. EFSA received comments from 17 parties including academia, national agencies, non‐governmental organisations and private bodies. This report lists the individual comments received and explains in detail how they were taken into account during the finalisation process of the scientific report. EFSA wishes to thank all the commenters for their valuable contributions.

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)0.115
metaresearch head score (Gemma)0.205
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.115
Threshold uncertainty score0.606

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1150.205
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.003
Science and technology studies0.0060.004
Scholarly communication0.0090.004
Open science0.0030.008
Research integrity0.0210.018
Insufficient payload (model declined to judge)0.0190.010

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 head0.066
GPT teacher head0.266
Teacher spread0.200 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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