Physical Characterization of Cottonseeds in Native Species (Gossypium spp.) From Mexico
Bibliographic record
Abstract
The objective of this research was to do the physical characterization of cotton species (Gossypium spp.) based on the attributes of the seed. The experiment was performed at the Forage Biochemistry Laboratory of the Valle of Mexico Experiment Station, using seed of four cotton species (G. hirsutum, G. aridum, G. lobatum and G. shwendimanii). This research was carried out in two phases: in the first one, the weight of 1000 seeds and the hectolitre weight were determined. In the second one, seed dimensions were obtained by digital images and analysis process. The four cotton species were tested under a completely random experimental design. The data analysis was made by the variance analysis method, Tukey multiple comparison tests of means, principal components and cluster analysis. The results showed significant differences (0.01%) in all the physical characters of the seeds. Based on the grouping analyzes, three groups with contrasting characteristics among the cotton species were identified, being G. hirsutum, the species that presented the highest value in the vector magnitude of the physical characters of the cottonseed. The area, width and weight of 1000 seeds were the main variables that explained 98.6% of the variability existing in the characteristics of the seed, so these physical attributes play an important role in the characterization of the Gossypium native species of Mexico.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".