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Record W2939054676 · doi:10.5539/jas.v11n5p49

Emergence Percentage and Speed of Rootstocks for Citriculture in South of Brazil

2019· article· en· W2939054676 on OpenAlexvenueno aff
Léo Omar Duarte Marques, Paulo Mello-Farias, R. P. de Oliveira, Maximiliano Dini, W. dos S. Soares Filho, Marcelo Barbosa Malgarim

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBanana Cultivation and Research
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsRootstockSeedlingSowingPhytosanitary certificationBiologyHybridHorticultureCultivar

Abstract

fetched live from OpenAlex

The fact that Brazilian citriculture has focused on few rootstocks poses several phytosanitary risks to the culture and has made genetic improvement programs develop hybrid genotypes to be used as novel alternatives. This study aimed at evaluating the behavior of 42 different rootstocks regarding their emergence percentage and speed in weather conditions experienced in the extreme south of Brazil. Seeds of hybrids developed by the Citrus Genetic Improvement Program (PMG Citros) at the Embrapa Mandioca e Fruticultura (PMG Citros) and cultivars from other citrus growing regions were sown in conical tubes filled with commercial substrate in May 2017. A thoroughly randomized design with 4 replicates, each composed of 45 sampling units, was employed. Emergence percentage and emergence speed of every genotype were evaluated and the ones with the highest emergence percentage of seedlings and the ones with the most precocious emergence were identified. All genotypes completed seedling emergence 98 days after sowing. Seeds of Trifoliata, TSKC × CTSW-041 and TSKC × CTSW-025 had the highest values of seedling emergence whereas the highest emergence speed indexes were exhibited by genotypes Trifoliata and lemon tree ‘Cravo’.

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)0.001
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 score0.911
Threshold uncertainty score0.150

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.021
GPT teacher head0.275
Teacher spread0.253 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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