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Record W2980214518 · doi:10.1590/0100-29452019543

Ammonia thiosulfate in Japanese plum tree thinning

2019· article· en· W2980214518 on OpenAlexaff
Ricardo Antônio Ayub, Iohann Metzger Bauchrowitz, Clandio Medeiros da Silva, Isabela Letícia Pessenti, Fernanda Grimaldi, André Belmont Pereira

Bibliographic record

VenueRevista Brasileira de Fruticultura · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsThinningTitratable acidRandomized block designHorticultureMathematicsCropTukey's range testBiologyAgronomy

Abstract

fetched live from OpenAlex

Abstract In order to test the use of ammonia thiosulfate (ATS) in the chemical thinning of the Japanese plum tree, a field experiment was conducted throughout the 2015/2016 and 2016/2017 crop growing seasons at Ponta Grossa, PR, Brazil. The experimental design adopted herein was a randomized block design (RBD). During the 2015/2016 season, the trial was comprised of two accesses and six treatments, such as T1 (control); T2 (ATS 4%); T3 (ATS 5%); T4 (ATS 6%); T5 (ATS 7%) and T6 (manual thinning) with four replications. During the 2016/2017 season, solely one plum tree access was taken into account along with six treatments, such as T1 (control); T2 (ATS 6%); T3 (ATS 7%); T4 (ATS 8%); T5 (ATS 9%) and T6 (manual thinning) and four replications. The evaluations were performed seven days before the application of ATS and 30 days before harvest to establish the percentage of fruit drop. The following response variables were assessed: diameter (D), fresh mass (MF), firmness (N), soluble solids (SS), titratable acidity (AT), ratio, pH and plant production (PP). Experimental data were compared by means of the Tukey test at 5% probability. The concentration of 8 % ATS was efficient in the chemical thinning of the Japanese plum fruits, having a diameter and productivity similar to the manual thinning, evidencing that such a product was amenable to enhance commercial fruit quality.

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 score0.674
Threshold uncertainty score0.515

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 communication0.0000.000
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 head0.017
GPT teacher head0.228
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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

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