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Record W2626247298 · doi:10.1139/cjas-2016-0150

ENZYMATIC EXTRACT OF Trametes maxima CU1 ON PRODUCTIVE PARAMETERS AND CARCASS YIELD OF RABBITS

2017· article· en· W2626247298 on OpenAlexvenueno aff
Carlos Alberto Hernández-Martínez, J Herrera, Gerardo Méndez‐Zamora, Carlos E. Hernández-Luna, Guadalupe Gutiérrez‐Soto

Bibliographic record

VenueCanadian Journal of Animal Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsnot available
FundersUniversidad Autónoma de Nuevo León
KeywordsAnimal scienceYield (engineering)Feed conversion ratioBody weightAmylaseEnzymeBiologyChemistryFood scienceBiochemistryEndocrinology

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the effect of the enzymatic extract (EnzE) of native fungus Trametes maxima CU1 on the productive parameters and carcass yield of rabbits. A total of 36 rabbits, 18 New Zealand and 18 California breeds were distributed randomly into two treatments: control (without EnzE supplementation) and EnzE2.5 (with 2.5% of enzymatic extract added to drinking water). All rabbits were fed with a commercial diet ad libitum. At 49, 71, and 91 d, data for body weight (BW), average daily feed intake (ADFI), feed efficiency, and average daily gain were collected. Moreover, dressing out percentage (DoP) and carcass fat yield (%CFY) were estimated. BW and ADFI were not different between treatments (P > 0.05). However, rabbits supplemented with EnzE2.5 showed higher values than the control. Rabbits EnzE2.5 and New Zealand males showed the best productive efficiency at 49 d (P < 0.05). On the other hand, EnzE2.5 showed greater DoP than control; furthermore, EnzE2.5 did not show any effect over %CFY. These results show the potential of T. maxima CU1 as a source of lignocellulases and amylases for the improvement of productive behavior and carcasses yield.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.048
GPT teacher head0.246
Teacher spread0.198 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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