Sapote mamey [<i>Pouteria sapota</i> (Jacquin) H.E. Moore & Stearn]: A potential fruit crop for subtropical regions of Michoacan, Mexico
Bibliographic record
Abstract
Genotypes of sapote mamey, Pouteria sapota [(Jacquin) H.E. Moore & Stearn] from central-western Michoacán in Mexico were characterized based on physical and chemical fruit characteristics. Cluster analysis of data indicated seven distinct genotype clusters. The greatest variability among clusters was attributed to physical and chemical fruit characteristics. Those were fruit weight, length, width, shape and texture; the ratio of fruit weight to seed weight; seed weight and length; mesocarp thickness and weight; epicarp weight; and the compositional components titratable acidity (TA), protein, total soluble solids (TSS), TSS to TA ratio and TSS to pH ratio. Canonical discriminant analysis was a used to identify the most desirable sapote mamey fruits based on physical (fruit and mesocarp weight) and compositional parameters. Two canonical discriminant functions explained >90% of the accumulated variation a mong the seven clusters of genotypes. Fruit weight, mesocarp thickness, and the ratio of total soluble solids to titratable acidity were dominant in the first function; and fruit weight and mesocarp thickness were dominant in the second. These morphological variables could be used for selecting sapote mamey trees with uniform fruit quality for either direct consumption or processing. Key words: Sapotaceae, sapote mamey, Pouteria sapota, cluster analysis, canonical discriminant analysis, fruit, morphological characterization
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".