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Record W2804381280 · doi:10.21640/ns.v10i20.1110

Efecto de tres dietas energético-proteicas en la población y producción de miel de colonias de abejas melíferas (Apis mellifera)

2018· article· es· W2804381280 on OpenAlexaff
Carlos Aurelio Medina-Flores, Ernesto Guzmán‐Novoa, Sergio Saldivar frausto, Jairo Iván Aguilera-Soto

Bibliographic record

VenueNova Scientia · 2018
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

El objetivo del presente trabajo fue comparar el desarrollo poblacional, peso y la producción de miel de colonias de abejas (Apis mellifera) alimentadas con tres dietas energético-proteicas a base de un sustituto elaborado con levadura de cerveza y polen, en combinación con jarabe de maíz de alta fructosa al 55% (JMAF), o jarabe de sacarosa (JA), o jarabe de sacarosa invertido (JAI). Se utilizaron 90 colonias homogeneizadas en cuanto a tamaño poblacional, reservas de alimento y origen de las reinas, todas alimentadas con el suplemento proteico, pero además, 30 de ellas recibieron JMAF, 30 JA y 30 JAI. La población de abejas adultas, área de cría operculada, peso y producción de miel de las colonias se determinó a los 27, 49 y 76 días después de haber sido establecidas. Las colonias alimentadas con JMAF fueron significativamente más pesadas que las alimentadas con JA y JAI entre las cuales no hubo diferencias. Las colonias alimentadas con JMAF produjeron significativamente más miel (35.8±3.35 kg) que las alimentadas con JA (28.2±2.65) y con JAI (24.8±2.70 kg), entre las cuales no hubo diferencias. Los resultados sugieren que el uso de JMAF en combinación con un suplemento proteico en la alimentación de estímulo de colonias de abejas melíferas representa una opción más eficiente que el uso de JA y JAI con suplemento proteico.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 head0.031
GPT teacher head0.327
Teacher spread0.296 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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