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Record W2162920207

Análisis de la productividad y la contribución financiera del componente arbóreo en pequeñas y medianas fincas ganaderas de la subcuenca del río Copán, Honduras

2011· article· es· W2162920207 on OpenAlexaff
Alejandro Ureña Chavarría, Guillermo Detlefsen, Muhammad Ibrahim, Glenn Galloway, Ronnie De Camino

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2011
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicAgroforestry and silvopastoral systems
Canadian institutionsCanadian AIDS Treatment Information Exchange
Fundersnot available
KeywordsGeographyHumanitiesForestryArt
DOInot available

Abstract

fetched live from OpenAlex

Se evaluó la productividad actual y potencial de los árboles maderables de sistemas silvopastoriles (SSP) en 35 fincas ganaderas (medianas y pequeñas), de la subcuenca del río Copán, Honduras, mediante un inventario de brinzales, latizales y fustales. Se encontró un total de 72 especies arbóreas pertenecientes a 62 géneros y 35 familias, de las cuales el 29% (21 especies), son consideradas maderables con valor comercial. El mayor potencial maderable comercial fue encontrado en el SSP de pasturas con árboles dispersos de Pinus oocarpa. Este SSP es el más abundante en las fincas estudiadas (77%), con una densidad promedio de latizales y fustales de 156 árboles ha-1 y de 43 brinzales ha-1. De igual forma, presenta un volumen comercial promedio de 71,5 m3 ha-1. De las 35 fincas inventariadas se seleccionaron ocho al azar (cuatro pequeñas y cuatro medianas), para conocer la contribución del componente maderable en la rentabilidad de las mismas. Los análisis financieros mostraron que en el caso de las fincas medianas, la contribución del VAN fue de 384,8 USD ha-1 y para las fincas pequeñas el aporte fue de 269,7 USD ha-1, equivalente a un 27 y 70% adicional a los ingresos obtenidos por la actividad ganadera, respectivamente.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0020.002
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 head0.022
GPT teacher head0.237
Teacher spread0.214 · 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.

Study designTheoretical or conceptual
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

Citations1
Published2011
Admission routes1
Has abstractyes

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Same venueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)Same topicAgroforestry and silvopastoral systemsFrench-language works237,207