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Record W2912944581 · doi:10.5539/jas.v11n3p494

Lettuce Growth in Different Pot Volumes and Irrigation Frequencies Under Saline Stress

2019· article· en· W2912944581 on OpenAlexvenueno aff
Ana Jacqueline de Oliveira Targino, Francisco de Assis de Oliveira, ⁠Mychelle Karla Teixeira de Oliveira, Lúcia R. de L. Régis, Helena Maria de Morais Neta, C. J. X. Cordeiro, Francisco Adênio Teixeira Alves, Luan Vítor Nascimento, Victor Gurgel Pessoa, Antonio Ariel Alves de Oliveira, Maria Williane de Lima Souza, Paulo Victor de Menezes, Jessilanne Plinia BM Costa, Isabelly Cristina da Silva Marques, Rafaelle da Silva Freitas

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
Fundersnot available
KeywordsIrrigationSalinityRandomized block designSaline waterSalineMathematicsAnimal scienceHorticultureEnvironmental scienceAgronomyBiologyEcology

Abstract

fetched live from OpenAlex

This study aimed to evaluate lettuce production in different pot volumes and different irrigation frequency under saline stress. The experiment was carried out at the Department of Environmental and Technological Sciences of the Federal Rural University of the Semi-Arid, in Mossoró, RN, Brazil. Randomized block experimental design was used in factorial scheme of 2 × 3 × 3, with 3 replicates. Treatments were consisted of 2 water’s salinity levels (0.5 and 2.0 dS m-1), 3 frequencies of irrigation (6, 9, and 12 daily events), and 3 different pots (0.5, 1.0, and 3.0 L). Data from parameters evaluated at harvest were submitted to variance analysis and means were compared. We verified that when the lower saline water (0.5 dS m-1) was used lettuce production increased when cultivated in pots of 3.0 L and submitted to 6 daily events of irrigation. When 3.0 dS m-1 water was used the best results were obtained in 0.5 L and nine daily events of irrigation. The combination of 0.5 L pot and six or nine daily events of irrigation better inhibited the damaging effects of salinity.

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 score0.640
Threshold uncertainty score0.131

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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