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Record W1967864130 · doi:10.1271/bbb.66.2342

Milk Calcium Taken with Cheese Increases Bone Mineral Density and Bone Strength in Growing Rats

2002· article· en· W1967864130 on OpenAlexaff
Ken Kato, Yukihiro Takada, Hiroaki Matsuyama, Yoshihiro Kawasaki, Seiichiro Aoe, H. Yano, Yasuhiro Toba

Bibliographic record

VenueBioscience Biotechnology and Biochemistry · 2002
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsOxford Frozen Foods (Canada)
Fundersnot available
KeywordsCalciumBone mineralOsteoporosisFemurMealChemistryBioavailabilityFood scienceAnimal scienceInternal medicineMedicineBiologySurgery

Abstract

fetched live from OpenAlex

We investigated the calcium bioavailability of milk calcium, taken with or without cheese. Twenty-four 6-week-old male rats for a meal-feeding experiment were trained to consume an AIN-76 diet within 2 h (2 times per day) for 2 weeks. The rats were then divided into three experimental groups, each fed 2 types of experimental diets: Control group, Cheese group, and Ca-Cheese group. The rats were each alternately given 2 types of experimental diets at 2-h meal-feeding for 31 days. The breaking force and energy of the femur in the Ca-Cheese group were significantly higher than in the control group. The bone mineral density (BMD) of the lumbar spine and the femur in the Ca-Cheese group was also significantly higher than in the other two groups. These results indicate that milk calcium taken with cheese increases bone strength and BMD efficiently, results that may be useful for the prevention of osteoporosis.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.252
Teacher spread0.235 · 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 designBench or experimental
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

Citations18
Published2002
Admission routes1
Has abstractyes

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