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Record W2949714775 · doi:10.36829/63cts.v4i2.502

Evaluación del parasitoide Aprostecetus esurus como controlador biológico del barrenador de la caña (Diatraea crambidoides) en jaulas bajo las condicones de la granja de Belén

2017· article· es· W2949714775 on OpenAlexaff
Francisco Pec

Bibliographic record

VenueCiencia Tecnologí­a y Salud · 2017
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsArt

Abstract

fetched live from OpenAlex

Este estudio se realizó bajo las condiciones de la Finca Belén del Ingenio La Unión, Santa Lucia Cotzumalguapa, Guatemala. Se simuló con tres niveles de 20,000 adultos de barrenador/ha para obtener un alto índice de infestación y así evaluar el efecto de tres proporciones de Aprostocetus esurus y Diatraea crambidoides, sobre el porcentaje de entrenudos dañados y la cantidad de toneladas de azúcar/ha. El ensayo se estableció en un cañaveral de cinco cortes de la variedad CP 72-2086 cultivada a un distanciamiento de 1.5 m entre surcos. Cada unidad experimental constó de un surco de 7.5 m de longitud, con ocho repeticiones bajo un diseño experimental de bloques completos al azar, cada tratamiento se estableció en una jaula de 90 m2 a manera de aislar el sistema albergando las repeticiones de cada tratamiento. Las liberaciones de A. esurus se realizaron entre 36 y 39 días después de haber liberado los adultos de barrenador a manera que la siguiente generación de barrenadores esté en el estadiode crisálida. Se determinó que las liberaciones de A. esurus producen un incremento estadísticamente significativo sobre el rendimiento en toneladas de azúcar/ha, obteniéndose este incremento al liberar al menos cinco A. esurus por cada crisálida proyectada.

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 score0.033
Threshold uncertainty score0.066

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 communication0.0010.000
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 head0.010
GPT teacher head0.288
Teacher spread0.277 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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