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Record W3081641231 · doi:10.1002/evan.21860

Synergies between the Developmental Origins of Health and Disease framework and multiple branches of evolutionary anthropology

2020· review· en· W3081641231 on OpenAlexaff
Luseadra McKerracher, Ruby L. Fried, Andrew Wooyoung Kim, Tina Moffat, Deborah M. Sloboda, Tracey Galloway

Bibliographic record

VenueEvolutionary Anthropology Issues News and Reviews · 2020
Typereview
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMainstreamEvolutionary anthropologyEvolutionary medicineSociologyEvolutionary theoryBiologyEpistemologyEvolutionary biology

Abstract

fetched live from OpenAlex

The Developmental Origins of Health and Disease (DOHaD) hypothesis derives from the epidemiological and basic/mechanistic health sciences. This well-supported hypothesis holds that environment during the earliest stages of life-pre-conception, pregnancy, infancy-shapes developmental trajectories and ultimately health outcomes across the lifespan. Evolutionary anthropologists from multiple subdisciplines are embracing synergies between the DOHaD framework and developmentalist approaches from evolutionary biology. Even wider dissemination and employment of DOHaD concepts will benefit evolutionary anthropological research. Insights from experimental DOHaD work will focus anthropologists' attention on biochemical/physiological mechanisms underpinning observed links between growth/health/behavioral outcomes and environmental contexts. Furthermore, the communication tools and wide public appeal of developmentalist health scientific research may facilitate the translation/application of evolutionary anthropological findings. Evolutionary Anthropology, in turn, can increase mainstream DOHaD research's use of evolutionary theory; holistic, longitudinal, and community-based perspectives; and engagement with populations whose environmental exposures differ from those most commonly studied in the health sciences.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.073
GPT teacher head0.397
Teacher spread0.324 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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