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Record W2774877394 · doi:10.5539/jas.v10n1p38

Tolerance of Varieties and Hybrid of Pumpkin and Squash to Salt Stress

2017· article· en· W2774877394 on OpenAlexvenueno aff
Adriana da Silva Santos, Francisco Vaniés da Silva Sá, Lauter Silva Souto, Maria K. do N. Silva, Rômulo Carantino Lucena Moreira, Geovani Soares de Lima, Luderlândio de Andrade Silva, Evandro Franklin de Mesquita

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
Fundersnot available
KeywordsSquashSalinityIrrigationGreenhouseSowingSaline waterAridAgronomyHybridHorticultureBiologyEcology

Abstract

fetched live from OpenAlex

Irrigated agriculture in arid and semi-arid regions is a practice encouraged to maximize food production, however these regions hesitate a qualitative shortage of water resources, often being necessary the use of saline water, affecting soil and sensitive plants to saline stress. So this study aimed to evaluate emergence, growth, phytomass accumulation and tolerance of the Tetsukabuto hybrid and varieties of pumpkin and squash under salt stress. The experiment was carried out in protected environment (greenhouse), located in the municipality of Pombal, Paraíba, Brazil. Treatments were arranged in completely randomized experimental design with four replicates, in a 5 × 3 factorial scheme, relative to five levels of irrigation water salinity and three genotypes of pumpkin and squash. Pumpkin and squash seedlings were grown on trays of 30 cells with capacity for 0.1 dm3 of substrate, during the first 20 days after sowing, and were monitored for emergence, initial growth, dry matter accumulation and tolerance to salinity. Increment in irrigation water salinity reduces emergence and initial growth of the pumpkin and squash genotypes. The genotypes G2 - ‘Squash cv. Coroa’ and G3 - Tetsukabuto hybrid are more tolerant to salinity than G1 - ‘Pumpkin cv. Jacarezinho’ in the initial growth stage.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.788
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

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 communication0.0000.001
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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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