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Caracterización de la actividad apícola en los principales municipios productores de miel en Campeche, México

2018· article· es· W2806891161 on OpenAlexaff
Jesús Froylán Martínez-Puc, William Cetzal‐Ix, Noel Antonio González-Valdivia, Fernando Casanova‐Lugo, Basu Saikat-Kumar

Bibliographic record

VenueJournal of the Selva Andina Animal Science · 2018
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

En Mxico, la apicultura es una de las principales actividades del sector agropecuario debido a su produccin de miel de alta calidad la cual es apreciada en diversos pases de Europa. Esta actividad es practicada por ms de 40000 productores, que cuentan con alrededor de dos millones de colmenas en apiarios distribuidos en las cinco regiones apcolas a nivel nacional (Norte, Centro y Altiplano, Pacfico, Golfo y Pennsula de Yucatn). La pennsula de Yucatn incluye los estados de Campeche, Yucatn y Quintana Roo, considerada como la regin ms importante debido a que destina cerca del 95 % de la produccin de miel al mercado internacional. La regin concentra del 30 al 35 % del total de las colonias de abejas a nivel nacional y es considerada como una actividad secundaria. Con la finalidad de caracterizar la actividad apcola en Campeche (en municipios de Campeche, Champotn y Hopelchn), se aplicaron encuestas a 120 productores de julio a diciembre del 2016. La edad promedio de los apicultores fue de 57 aos, con un promedio de 2.27 apiarios por productor, con 20.6 colmenas por apiario. Los apicultores dedican dos das a la semana a esta actividad y realizan en promedio 3.67 cosechas por ao. Con base en lo anterior, se recomienda fomentar el relevo generacional en la actividad apcola, que se enfoque en jvenes apicultores.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.017
GPT teacher head0.321
Teacher spread0.304 · 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.

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

Citations10
Published2018
Admission routes1
Has abstractyes

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