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Record W3109300711 · doi:10.1016/j.crfs.2020.11.003

Characterization of the ergometric properties of commercial bioactive dairy peptides

2020· article· en· W3109300711 on OpenAlexafffund
Luis E. Maya, Martin G. Scanlon

Bibliographic record

VenueCurrent Research in Food Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCompressibilityThermodynamicsChemistryVolume (thermodynamics)Adiabatic processChromatography

Abstract

fetched live from OpenAlex

The thermodynamic properties of bioactive peptides provide insights into their functional behavior and their biological efficacy. We conducted precise analyses of the density, the ultrasonic velocity and the relative attenuation of serial dilutions of three commercial dairy peptides prepared by enzymatic methods. From these we determined the partial specific volume and the partial specific adiabatic compressibility coefficient for the peptides. At concentrations greater than ~2.5 ​mg ​mL−1, the apparent values for specific volume and adiabatic compressibility were constant, differing between the three peptides at ±3% for specific volume and ±70% for compressibility. Both specific volume and adiabatic compressibility were highly dependent on concentration, indicating the importance of precise low concentration measurements to obtain correct values for these thermodynamic parameters. From these parameters it was apparent that restructuring of water molecules around the peptides (and their associated counterions) led to compact solutes that were also incompressible. These thermodynamic analyses are critical for understanding how the properties and the beneficial effects of bioactive peptides are influenced by their chemical environment.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.452
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.428
GPT teacher head0.377
Teacher spread0.050 · 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 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

Citations1
Published2020
Admission routes2
Has abstractyes

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