Estimation of Tobacco Leaf Area by a Non-destructive Method
Bibliographic record
Abstract
The leaf area index (AFI) is an important variable when evaluating the growth, development and possible productivity of a crop. The objective of this article was to determine mathematical models that allow estimating the leaf area of tobacco from measurements of dimensions of length and width of tobacco leaves. The experimental design used was in blocks with four replications and two planting times. The treatments consisted of five commercial cultivars of Virginia type tobacco and Burley type. Of which the leaves were detached from the stem, and these leaves were fractionated in limbo and petiole. Its leaves were 10 models were obtained to estimate the leaf area of tobacco. All the models presented a significant test to estimate the leaf area. The power and quadratic models presented values of determination coefficients higher than linear models. It had the best results with the nonlinear power and quadratic model, using the squared width multiplied by length. Therefore, the measurements of the leaf area of tobacco can be estimated from potential, linear and quadratic equations with good precision. The models that use linear squared width measurements multiplied by leaf limbo length satisfactorily estimate the leaf area of tobacco.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".