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Record W2059683079 · doi:10.3390/ijms12084885

The Chemical Composition and Nitrogen Distribution of Chinese Yak (Maiwa) Milk

2011· article· en· W2059683079 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDigestive system and related health
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Natural Science Foundation of China
KeywordsLactoseFood scienceAmino acidCaseinComposition (language)Essential amino acidChemistryYAKIngredientNutrientBiologyAnimal scienceBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The paper surveyed the chemical composition and nitrogen distribution of Maiwa yak milk, and compared the results with reference composition of cow milk. Compared to cow milk, yak milk was richer in protein (especially whey protein), essential amino acids, fat, lactose and minerals (except phosphorus). The contents of some nutrients (total protein, lactose, essential amino acids and casein) were higher in the warm season than in the cold season. Higher ratios of total essential amino acids/total amino acids (TEAA/TAA) and total essential amino acids/total non essential amino acids (TEAA/TNEAA) were found in the yak milk from the warm season. However its annual average ratio of EAA/TAA and that of EAA/NEAA were similar to those of cow milk. Yak milk was rich in calcium and iron (p < 0.05), and thus may serve as a nutritional ingredient with a potential application in industrial processing.

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.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.126

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.275
Teacher spread0.266 · 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