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Environmental Epidemiology

2003· book-chapter· en· W4388331426 on OpenAlexaff
Donald T. Wigle

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsInstitute of Population and Public HealthUniversity of Ottawa
Fundersnot available
KeywordsEnvironmental epidemiologyEpidemiologyEnvironmental healthStrengths and weaknessesPopulationScope (computer science)Human healthPsychological interventionMedicinePsychologyPathology

Abstract

fetched live from OpenAlex

Abstract Epidemiology is the study of the distribution and determinants of health conditions in human populations as a basis for preventive and other interventions. Unique to environmental epidemiology is its focus on environmental factors to which humans are unwittingly exposed. The scope of environmental epidemiology addressed in this book includes studies of adverse health outcomes in human populations and their relationship to prenatal and childhood exposures to environmental hazards. The purpose of this chapter is to describe the types of environmental epidemiologic studies, their strengths and weaknesses, their role in describing population distributions of exposures and health outcomes, and their use to generate and test hypotheses about environmental threats to child health. The discussion includes important issues underlying the limited ability of epidemiologic studies to identify hazards when population exposures are low and health outcomes are subtle or delayed.

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)0.001
metaresearch head score (Gemma)0.002
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: Other
Teacher disagreement score0.129
Threshold uncertainty score0.433

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1290.051

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 head0.261
Teacher spread0.225 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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