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Record W3208922011 · doi:10.5539/jas.v10n2p116

Climatic and Soil Water Balances for the Melon Crop

2018· article· en· W3208922011 on OpenAlexvenueno aff
Jaedson Cláudio Anunciato Mota, Paulo Leonel Libardi, Raimundo Nonato Assís Júnior, Alexsandro dos Santos Brito, Márcio Godofrêdo Rocha Lobato, Thiago Leite de Alencar, Alcione Guimarães Freire, Juarez Cassiano Lima Júnior

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural and Food Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsWater balanceEvapotranspirationEnvironmental scienceSoil waterCrop coefficientContext (archaeology)MelonHydrology (agriculture)CropWater useAgronomySoil scienceGeographyEcologyGeology

Abstract

fetched live from OpenAlex

The correct estimate of the water requirements of a crop, besides favoring its full development, also allows the rational use of water. In this context, this study aimed to evaluate water balance in the soil and estimated through climatic methods for the melon crop. Field water balance was daily determined along a period of 70 days. Climatic water balance was determined based on the reference evapotranspiration estimated by the methods of Penman-Monteith, Thornthwaite and Hargreaves-Samani. It was concluded that climatic methods do not estimate correctly water storage in the soil and, consequently, also the balance. Therefore, they should not substitute the soil water balance method to determine these variables. The water management for the melon crop based on evapotranspiration estimated through climatic methods results in overestimation of the water depth to be applied in the soil, in the initial growth stage, and underestimation in the periods of highest water demand.

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 score0.005
Threshold uncertainty score0.010

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)0.0000.000

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 head0.020
GPT teacher head0.231
Teacher spread0.210 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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