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Record W1550149305 · doi:10.1002/9781118398814.ch15

Biological Timekeeping: Individual Variation, Performance, and Fitness

2014· other· en· W1550149305 on OpenAlexaff
Scott A. MacDougall‐Shackleton, Heather E. Watts, Thomas P. Hahn

Bibliographic record

VenueIntegrative Organismal Biology · 2014
Typeother
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsWestern University
Fundersnot available
KeywordsChronobiologyCircadian rhythmRhythmBiologyBacterial circadian rhythmsBiological clockDiversification (marketing strategy)Variation (astronomy)EcologyEvolutionary biologyPopulationSeasonalityOrganismNeuroscienceCircadian clockMedicineInternal medicine

Abstract

fetched live from OpenAlex

Virtually all parts of our planet exhibit cyclic changes in environmental conditions, and these cycles have persisted throughout the evolution and diversification of life. In response, most biological processes exhibit rhythms, and these rhythms can be observed at molecular, cellular, whole-organism, and population scales. As well, these rhythms exist at multiple time scales, including short-term oscillations, tidal, daily, lunar, and annual cycles. Biological rhythms have been extensively studied for many decades at a range of levels, including examination of the molecular basis of circadian clocks, neural and endocrine control of circadian cycles, seasonality and annual rhythms. This chapter briefly introduces fundamental concepts of the integrative physiology of circadian rhythms and seasonality with an emphasis on examples where individual variation in biological timekeeping relates to variation in performance or fitness.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.026
GPT teacher head0.256
Teacher spread0.230 · 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 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

Citations5
Published2014
Admission routes1
Has abstractyes

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