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Record W2587943894

Diversidad forrajera tropical 1: Selección y uso de leñosas forrajeras en sistemas de alimentación ganadera para zonas secas de Nicaragua

2013· article· es· W2587943894 on OpenAlexaff
Nelson Pérez Almario, Mohammad Ibrahim, Cristóbal Villanueva, Christina Skarpe, Hubert Guérin

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicAgroforestry and silvopastoral systems
Canadian institutionsCanadian AIDS Treatment Information Exchange
Fundersnot available
KeywordsBiologyLeucaena leucocephalaAgronomy
DOInot available

Abstract

fetched live from OpenAlex

Se evaluaron diez especies con potencial forrajero para zonas secas de Rivas, Nicaragua con el fin de integrarlas en el diseno de sistemas silvopastoriles como estrategias de alimentacion bovina. Las especies evaluadas fueron leguminosas sin espinas (Albizia niopoides, Gliricidia sepium, Leucaena leucocephala, Samanea saman); leguminosas con espinas (Acacia farnesiana, Mimosa pigra); lenosas no leguminosas (Moringa oleifera, Brosimun alicastrum, Cordia dentata y Guazuma ulmifolia). Se utilizo forraje de ramas delgadas menores a 1,0 cm de diametro de diferentes individuos seleccionados de cada especie lenosa. El forraje se ofrecio, mediante el metodo de cafeteria, a cinco vacas en produccion con similares caracteristicas de raza, peso, edad, sexo y estado de lactancia. Se evaluo la preferencia, el tiempo de consumo de cada lenosa y el numero y tamano de bocados por especie; el consumo se obtuvo por diferencia entre la cantidad de forraje ofrecido y restante. Los resultados reflejan una mayor preferencia y consumo de forraje de tres especies (S. saman, L. leucocephala y A. niopoides) que presentan diferencias fisicas, nutricionales y fenologicas en relacion con las demas. Estas especies representan las mejores opciones para el diseno de sistemas silvopastoriles en el tropico seco nicaraguense.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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