Modeling the Growth of Sudangrass Cultivars at Sowing Times
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".