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Importancia económica y biológica de la alfalfa en el centro de Chihuahua

2017· article· es· W3115473721 on OpenAlexaff
Sandra Alvarado Ríos, Sergio Guerrero Morales, José Nájera, Bertha Alicia Rivas Lucero, Abdón Palacios Monarrez

Bibliographic record

VenueRevista Biológico Agropecuaria Tuxpan · 2017
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicAgricultural and Food Production Studies
Canadian institutionsMedicago (Canada)
Fundersnot available
KeywordsGeographyHumanitiesBiologyArt

Abstract

fetched live from OpenAlex

El cultivo de alfalfa en la región centro sur del estado de Chihuahua, es el de mayor superficie sembrada (35, 865.50 ha.) y representa una fuente de ingresos para familias enteras. La región es de las principales abastecedoras de alfalfa de los estados de Coahuila y Durango. El presente estudio se realizó con los objetivos de: Describir la distribución del ingreso entre productores e intermediarios por venta de alfalfa. Analizar el significado hídrico de la exportación de alfalfa a otras regiones. Determinar la importancia del cultivo de alfalfa en el control biológico de plagas en el nogal. Se realizaron entrevistas semiestructuradas a productores, intermediarios de alfalfa, e investigadores. La información obtenida, se analizó estadísticamente con los programas de Excel y SAS. El precio de venta de alfalfa, al consumidor final oscila de entre $ 2.5 y $ 4.00 por kg de alfalfa henificada. La producción total de alfalfa en el 2015 fue de 609 713.66 toneladas en base a peso seco. El 51% de la producción se exporto a otros estados, que equivalen a 219 496 921 m3 de agua exportada. La venta del 51% de alfalfa a otros estados genera un ingreso a la región de $971, 731,200.00 (con un 20% de ganancia del intermediario) a $1 195, 976,923.00 (con un 35% de ganancia del intermediario). Se encontró que la alfalfa es un hábitat muy importante de insectos que regulan a insectos plagas en el cultivo de nogal.

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 score0.260
Threshold uncertainty score0.518

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.280
Teacher spread0.268 · 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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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