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Record W2965909666 · doi:10.5539/jas.v11n14p83

Modeling the Growth of Sudangrass Cultivars at Sowing Times

2019· article· en· W2965909666 on OpenAlexvenueno aff
Rafael Vieira Pezzini, Alberto Cargnelutti Filho, Fernanda Carini, Cirineu Tolfo Bandeira, Jéssica Andiara Kleinpaul, Daniela Lixinski Silveira

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLeaf Properties and Growth Measurement
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado do Rio Grande do SulConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsGompertz functionSowingMathematicsCultivarShootLogistic functionHorticultureStatisticsNonlinear regressionDry weightConfidence intervalRegression analysisBiology

Abstract

fetched live from OpenAlex

This study aimed to fit the Gompertz and Logistic growth models to evaluate the description of fresh and dry masses of shoot as a function of accumulated thermal sum and accumulated solar radiation, to compare the fittings, and to indicate which one best describes the growth of two sudangrass cultivars at four sowing times. Eight uniformity trials were conducted with the sudangrass crop. Five plants were collected from each trial for weighing of fresh and dry shoot masses. These evaluations were carried out three times a week starting from 15 days after plant emergence. The Gompertz and Logistic models were fitted to the masses as a function of accumulated thermal sum and accumulated solar radiation. The parameters and their confidence intervals were estimated. The points of maximum acceleration, inflection, maximum deceleration and asymptotic deceleration, and fit quality indicators were calculated. The intrinsic nonlinearity and the parameter-effects nonlinearity were quantified. The independent variables accumulated thermal sum and accumulated solar radiation can be used to fit the models. Both models satisfactorily describe the growth of fresh and dry shoot masses of cultivars BRS Estribo and CG Farrapo. The Logistic model is more accurate.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.247

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 communication0.0000.001
Open science0.0010.000
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 head0.025
GPT teacher head0.208
Teacher spread0.183 · 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 designBench or experimental
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

Citations1
Published2019
Admission routes1
Has abstractyes

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