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Record W2167434298 · doi:10.1111/are.12365

Bioavailability of arginine from Indian mustard protein concentrate and meal compared with that of a soy protein concentrate in rainbow trout (<i>Oncorhynchys mykiss</i>)

2014· article· en· W2167434298 on OpenAlexafffund
M.A. Kabir Chowdhury, Kattia Preciado Iñiguez, C. F. M. de Lange, Vernon Osborne, A. Lemme, Dominique Bureau

Bibliographic record

VenueAquaculture Research · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsUniversity of Guelph
FundersEvonik IndustriesMitacsMinisterstvo Školství, Mládeže a TělovýchovyMinistry of Natural Resources
KeywordsSoy proteinRainbow troutBioavailabilityBiologyArginineFood scienceMealFish mealAnimal scienceBiochemistryFish <Actinopterygii>Amino acidFisheryPharmacology

Abstract

fetched live from OpenAlex

Relative bioavailability (RBV) of arginine (Arg) from Indian mustard protein concentrate (IMC, 62% crude protein) and Indian mustard meal (IMM, 42% crude protein), and a commercially available soy protein concentrate (SPC, 57% crude protein) was compared with that of crystalline L-arginine (L-Arg) in rainbow trout. A basal diet highly deficient in Arg (1.23%) was formulated. Eight other isoproteic and isoenergetic diets were formulated to contain ~1.35% and ~1.5% Arg by adding increasing amount of IMC, IMM, SPC and L-Arg. The experimental diets were fed for 16 weeks using a standard protocol. Growth rate, weight gain (g fish−1) and protein (PD, g fish−1) and lipid (LD, g fish−1) deposition were increased linearly with increasing level of Arg from all ingredients. Arg availability from protein-bound sources were equal or higher than those from L-Arg. RBV of Arg from IMC, IMM and SPC were ranged from 100% and 123% than that from L-Arg (assumed as 100% bio-available). Among the ingredients, only the RBV of Arg from IMC was significantly higher than those from SPC (P < 0.05). The findings suggest that the RBV of Arg from IMC and IMM are very good and comparable to that of the commercial SPC used in this study.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.270
Teacher spread0.228 · 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

Citations10
Published2014
Admission routes2
Has abstractyes

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