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Record W4223500776 · doi:10.1016/j.bcp.2022.115015

Obesity II: Establishing causal links between chemical exposures and obesity

2022· review· en· W4223500776 on OpenAlexafffund
Jerrold J. Heindel, Sarah Howard, Keren Agay‐Shay, Juan Pedro Arrebola, Karine Audouze, Patrick J. Babin, Robert Barouki, Amita Bansal, Étienne Blanc, Matthew C. Cave, Saurabh Chatterjee, Nicolas Chevalier, Mahua Choudhury, David Collier, Xavier Coumoul, Gabriella Garruti, Michael Gilbertson, Lori Hoepner, Alison C. Holloway, George Howell, Christopher D. Kassotis, Min Ji Kim, Dominique Lagadic‐Gossmann, Sophie Langouët, Antoine Legrand, Zhuorui Li, Hélène Le Mentec, Lars Lind, Peter Lind, Robert H. Lustig, Corinne Martin‐Chouly, Vesna Munić Kos, Normand Podechard, Troy A. Roepke, Robert M. Sargis, Anne P. Starling, Craig R. Tomlinson, Charbel Touma, Jan Vondráček, Frederick vom Saal, Bruce Blumberg

Bibliographic record

VenueBiochemical Pharmacology · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsMcMaster University
FundersNational Center for Complementary and Integrative HealthNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of General Medical SciencesNational Institute of Mental HealthCanadian Institutes of Health ResearchNational Institutes of HealthNational Institute of Food and AgricultureNational Cancer InstituteNorris Cotton Cancer CenterGrantová Agentura České RepublikyEuropean CommissionNational Institute of Environmental Health SciencesEuropean Food Safety AuthorityU.S. Department of Agriculture
KeywordsObesityOvereatingEndocrine systemEndocrinologyBiologyMedicineEnvironmental healthInternal medicineHormone

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.036
GPT teacher head0.388
Teacher spread0.352 · 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
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

Citations191
Published2022
Admission routes2
Has abstractno

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