Artificial Neural Network and Multivariate Models Applied to Morphological Traits and Seeds of Common Beans Genotypes
Bibliographic record
Abstract
The aimed to characterize common beans genotypes utilizing multivariate models and artificial neural network thru the agronomic attributes and seeds dimensions. The experiment was conducted in the 2017/2018 crop season at the city of Tenente Portela - RS. The experimental design was expanded blocs, were 53 segregating F2 populations and ten cultivars considered checks, disposed in four repetitions. The accurate characterization of bean genotypes can be based in the reproductive period, cycle and seeds length. Genotypes with longer cycle increase the potential of ramifications, legume and seeds magnitude per plant and increase the seeds yield independent of the commercial group. The use of biometric approach allows revealing patterns to the genotype grouping, being the patterns magnitude dependent of the intrinsic premises to the Standardized Average Euclidian Distance, Tocher optimized grouping and Artificial Neural Network with non-supervised learning. It is defined that the Artificial Neural Network are determinant to define associative patterns, being these inferences indispensable to the common beans genotype selection that answer the agronomic attributes and seeds production.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".