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Record W3013916820 · doi:10.1080/10408444.2020.1740973

Thresholds of adversity and their applicability to endocrine disrupting chemicals

2020· review· en· W3013916820 on OpenAlexaboutno aff
Susy Brescia

Bibliographic record

VenueCritical Reviews in Toxicology · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsnot available
Fundersnot available
KeywordsEndocrine systemEuropean unionHazardRisk assessmentEndocrine disruptorRisk analysis (engineering)MedicineComputer scienceEconomicsBiologyInternal medicineHormoneComputer securityEconomic policy

Abstract

fetched live from OpenAlex

a hazard-based approach. Presumably this is due to an assumed lack of thresholds for the adverse effects of such substances. Conversely, in other jurisdictions, such as USA, Canada, Australia and Japan, endocrine disruptors (EDs) are regulated using a risk-based approach. As a consequence, in recent years there has been increasing controversy on whether thresholds can be inferred for endocrine-mediated effects. There is concern that the endocrine system is too complex to allow estimation of safe levels of exposure to such chemicals. This brief review aims to evaluate the available scientific evidence in this area and offer a sound and robust conclusion supported by this analysis. It is concluded that there is nothing special or unique about endocrine disruption or greater uncertainties in its assessment compared to other non-genotoxic forms of toxicity to justify adopting a non-threshold approach by default. Biology predicts that thresholds of adversity exist and are the rule for all endpoints, including those arising from endocrine disruption. A threshold approach to the risk assessment of endocrine disrupting chemicals is scientifically justified.

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)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.434
Teacher spread0.393 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations35
Published2020
Admission routes1
Has abstractyes

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