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Rendimiento a la carcasa de los cuyes alimentados con gramíneas tropicales Axonopus scoparius, Pennisetum SP, Pennisetum purpureum y Tripsacum laxum en Morona Santiago

2020· article· es· W3096549251 on OpenAlexvenueno aff
Tamia Noboa Abdo, Luis Rojas Oviedo, Luis Condo Plaza, Segundo Shagñay Rea

Bibliographic record

VenueConcienciaDigital · 2020
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicAgricultural and Food Production Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPennisetum purpureumBiologyPennisetumAnimal scienceHorticultureDry matter

Abstract

fetched live from OpenAlex

El rendimiento a la carcasa de cuyes machos que fueron alimentados con gramíneas tropicales en Morona Santiago, se realizó en la ESPOCH Sede Morona Santiago, para esto se utilizaron cuatro pastos: Axonopus scoparius (Gramalote – Tratamiento1), Pennisetum sp (Maralfalfa- Tratamiento 2), Pennisetum purpureum (Pasto elefante- Tratamiento 3) y Tripsacum laxum (Pasto Guatemala- Tratamiento 4) con diez repeticiones cada uno, dando un total de 40 cuyes machos, los mismos que consumieron los pastos señalados desde el destete hasta el sacrificio (150 días de edad), se distribuyeron completamente al azar y sus resultados se analizaron a través de la varianza y la prueba de Tukey (p<0.05); determinándose que la utilización del Axonopus scoparius permitió un peso al destete de 247,84 g, el peso de la canal al sacrificio de 1107.52, 599.63 g, su rendimiento sin ayuno de 54 %, el peso y rendimiento de las vísceras, pelo y sangre fue de 507.89 g y 45.70 %, el peso y rendimiento únicamente de vísceras fue de 375,83 g y 33.80 %, el peso y rendimiento del pelo fue de 78.14 g y 7.06 % y el peso y rendimiento de la sangre fue de 53.92 g y 4.84 % respectivamente, señalándose que este pasto fue más eficiente.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.257
Teacher spread0.229 · 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 designObservational
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

Citations2
Published2020
Admission routes1
Has abstractyes

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