Genetic Diversity and Subspecific Races of Upland Cotton (Gossypium hirsutum L.)
Bibliographic record
Abstract
Background/Objectives: The classification and phylogenetic relationships of Gossypium hirsutum L. landraces, despite their proximity to southern Mexico, remain unresolved. This study aimed to clarify these relationships using SSR markers and hybridization methods, focusing on subspecies and race differentiation within G. hirsutum L. Methods: Seventy polymorphic SSR markers (out of 177 tested) were used to analyze 141 alleles and calculate genetic distances among accessions. Phylogenetic relationships were determined using MEGA software (version 11.0.13) and visualized in a phylogenetic tree. ANOVA in NCSS 12 was used for statistical analysis. Over 1000 inter-race crosses were conducted to assess boll-setting rates. Results: Distinct phylogenetic patterns were identified between G. hirsutum subspecies and races, correlating with boll-setting rates. Latifolium, richmondii, and morilli showed no significant increase in boll-setting rates in reciprocal crosses. Cultivars Omad and Bakht, as paternal parents, yielded higher boll-setting rates. Religiosum and yucatanense displayed high boll- and seed-setting rates as maternal parents but low rates as paternal parents. Additionally, phylogenetic analysis revealed a close relationship between cultivars ‘Omad’ and ‘Bakht’ with G. hirsutum race richmondii, indicating their close evolutionary relationship. Conclusions: Reciprocal differentiation characteristics of G. hirsutum subspecies and races, particularly religiosum and yucatanense, should be considered during hybridization for genetic and breeding studies. Understanding the phylogenetic relationships among G. hirsutum taxa is crucial for exploring the genetic diversity of this economically important species.
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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) | 0.001 | 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".