Selection of Morphoagronomic Descriptors in Physalis angulata L. Using Multivariate Techniques
Bibliographic record
Abstract
This study aimed at selecting determinant morphoagronomic descriptors to characterize and evaluate Physalis angulata L. germplasm. Twelve quantitative and twenty-two qualitative descriptors were analyzed in six accessions of P. angulata coming from the physalis germplasm collection belonging to the State University of Feira de Santana-BA. The selection and discharge of quantitative descriptors was based on the direct selection and on the Singh method, while qualitative descriptors were analyzed through entropy. The statistic analyses were carried out using the GENES and R programs. Ten quantitative descriptors were excluded through direct selection and five through the Singh method. However, only four descriptors were considered redundant by both methods: east-west fruit, weight of five ripe fruits, width of leaf blade and total soluble solids. Although the total soluble solids descriptor was appointed for discharge, it was included in the group of descriptors selected due to its importance in the characterization of physalis fruit. The list of minimum descriptors to describe physalis accessions comprised 15 descriptors: plant height, stem diameter, north-south fruits, number of fruits per plant, leaf blade length, internode length, fruit longitudinal length, fruit transversal length, total soluble solids, growth habit, stem color, leaf margin shape, unripe calyx color, unripe fruit shape and color. These were nine quantitive and six qualitative descriptors, respectively. The discharge of 55.88% of the descriptors did not cause significant loss of information and might allow the reduction of time and resources spent to characterize and evaluate physalis germplasm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".