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Impacto de los sistemas de riego por goteo en arándanos

2011· article· es· W1967845309 on OpenAlex
A. Pannunzio, F. Vilella, Pamela Texeira, Zdenka Premuzik

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRevista Brasileira de Engenharia Agrícola e Ambiental · 2011
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsImpact
Fundersnot available
KeywordsHorticultureHumanitiesPhysicsBiologyArt

Abstract

fetched live from OpenAlex

El ensayo examinó los efectos de diferentes sistemas de riego por goteo en el rendimiento de la variedad O´Neal de arándano (Vaccinium corymbosum L). El estudio se realizó en Zarate (33º 41’ S y 59º 41’ W), provincia de Buenos Aires, Argentina. El cultivo se implantó en agosto de 2001, la primera cosecha se efectuó en octubre de 2003. El objetivo fue determinar cual era el sistema de riego por goteo que maximizaba el rendimiento, siendo los tratamientos, un lateral de tubería de goteo por fila de plantas con goteros a 40 cm (T1) y dos laterales de tubería de goteo con goteros a 20 cm por fila de plantas (T2). El porcentaje de suelo mojado por sistema se incrementa a medida que aumenta el número de emisores por metro lineal. El diseño experimental fue de bloques completamente aleatorizados, con bloques de cinco plantas y cinco repeticiones por tratamiento. La cosecha de 2003, reportó rendimientos de 2436 kg ha-1 para el tratamiento T1 y de 4335 kg ha-1 para tratamiento T2. El mojado parcial del suelo que realiza el tratamiento T1, de menor cantidad de goteros por metro lineal, no es suficiente para mojar un porcentaje de suelo compatible con altos rendimientos.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.267
Teacher spread0.238 · 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