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Record W2495161526 · doi:10.5539/ijps.v8n3p154

Effects of Drinking Cows’ Milk at Breakfast in Promoting Sleep-Health in Japanese University Athletes

2016· article· en· W2495161526 on OpenAlexvenueno aff
Takahiro Kawada, Yuri Takamori, Miyo Nakade, Fujiko Tsuji, Milada Krejčí, Teruki Noji, Hitomi Takeuchi, Tetsuo Harada

Bibliographic record

VenueInternational Journal of Psychological Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsChronotypeMorningAthletesClubPsychologyMedicineMealAnimal sciencePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

<p>This study evaluates the effects of milk intake for 20 days at breakfast on diurnal type (chronotype), sleep habits and soccer performance in Japanese university male athletes attending a university soccer club. Seventy three athletes were asked to take 200 ml of cows’ milk at breakfast for 21 consecutive days during November and December, 2014. Twenty athletes attending the same soccer club did not drink cows’ milk for the same period of time and acted a control group.<strong> </strong>An integrated questionnaire was administered twice, before the intervention period and 1 month after it to all 93 participants. The questionnaire included questions on sleep habits and diurnal type. On the 10th day and 21st days of the intervention period, a questionnaire on performance/skill was administered to all participants. The group which drank cows’ milk showed higher frequency of improvement of soccer performance than did the control group did (performance—where higher values indicate less skill: milk drinking group=29.92, control group=31.9 on day 10; milk drinking group=28.21, control group=31.9 on day 21), and also judged that their soccer performance had improved more after 21 days than 10 days of the intervention. Those participants who changed diurnal type to becoming more morning-typed were more likely to judge that their soccer performance had improved than did those who showed no change in diurnal type.</p>

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.344
Teacher spread0.318 · 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.

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

Citations6
Published2016
Admission routes1
Has abstractyes

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