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Record W3177472145 · doi:10.15381/rivep.v32i2.20018

Efecto de cinco niveles de balance electrolítico dietario en el crecimiento, características de carcasa y metabolitos de suero sanguíneo del cuy (Cavia porcellus)

2021· article· es· W3177472145 on OpenAlexaff
Manuel Paredes, José Mantilla, Irma Bustamante, Joe Mantilla, José Cayotopa, Cristian Hobán, Pedro Ortiz, A. F. Mustafa

Bibliographic record

VenueRevista de Investigaciones Veterinarias del Perú · 2021
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicRabbits: Nutrition, Reproduction, Health
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesPhysicsAnimal scienceChemistryArtBiology

Abstract

fetched live from OpenAlex

Se realizó un estudio para determinar los efectos del balance electrolítico dietario (BED) sobre el rendimiento productivo, características de la canal y metabolitos sanguíneos del cuy en una granja experimental de Cajamarca, Perú. Se utilizaron 150 cuyes machos distribuidos en cinco tratamientos con cinco niveles de BED: 100, 200, 300, 400 y 500 mEq/kg. Cada tratamiento tuvo seis repeticiones y cada repetición con cinco cuyes/poza. Los cuyes consumieron alimento concentrado durante 42 días. Al término del periodo experimental se sacrificaron seis cuyes por tratamiento para determinar el rendimiento de carcasa, pesos relativos de las vísceras y concentración de metabolitos sanguíneos. Se encontraron diferencias (p<0.05) en el peso corporal final, ganancia de peso e índice de conversión alimenticia a favor de cuyes con BED de 300 mEq/kg. No hubo diferencias significativas entre tratamientos en las características de la canal ni en los valores de metabolitos en sangre, excepto la creatinina. Según los resultados de este estudio, el cuy podría ser alimentado con 300 mEq/kg de BED, con resultados satisfactorios de crecimiento.

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 score0.005

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.287
Teacher spread0.260 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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