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Record W2593514685 · doi:10.1504/ijram.2017.082569

Challenges and opportunities in the risk assessment of existing substances in Canada: lessons learned from the international community

2017· article· en· W2593514685 on OpenAlexafffundabout
Tara S. Barton Maclaren, Margit Westphal, Elaha Sarwar, Donald R. Mattison, Weihsueh A. Chiu, David J. Dix, Robert J. Kavlock, Daniel Krewski

Bibliographic record

VenueInternational Journal of Risk Assessment and Management · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsUniversity of OttawaHealth Canada
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of North Carolina at Chapel HillUniversity of OttawaNational Academy of SciencesU.S. Environmental Protection AgencyPrinceton UniversityOffice of Science
KeywordsLegislationEuropean unionAuthorizationPolitical scienceParliamentEnvironmental planningPublic administrationBusinessEnvironmental protectionLawInternational tradeGeography

Abstract

fetched live from OpenAlex

The evaluation and regulation of chemical substances have undergone a major overhaul in Canada, the USA and the European Union (EU) over the last decade. To facilitate increasing concerns over chemical safety, changes in regulations and strategic plans were introduced. Specifically, the Canadian parliament adopted a new amendment to the Canadian Environmental Protection Act (CEPA) in 1999, the US National Academy of Sciences published the 2007 NRC report TT21C, the US EPA developed the Strategic Plan in 2009, and the EU introduced new legislation in 2007 called Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH substances). A 2013 workshop held in Ottawa (Risk Sciences International) focused on regulatory issues and challenges faced by these nations. This review summarises many of the discussions held during the workshop and specifically, five challenges that Canada has encountered when assessing chemicals with limited data on Canada's Domestic Substances List (DSL).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.462
GPT teacher head0.476
Teacher spread0.015 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2017
Admission routes3
Has abstractyes

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