Genetic structure and diversity of Calycolpus moritzianus (Myrtaceae) in the north-eastern Andes of Colombia
Bibliographic record
Abstract
The Arrayán tree (Calycolpus moritzianus) is an endemic species from northern South America and it is important for its potential in the medical and cosmetic industry. However, to take advantage of its applied potential different biological aspects, such as genetic diversity, must be characterized. We evaluated 5 RAMs markers on 45 individuals of C. moritzianus collected from 5 locations in Norte de Santander, Colombia, to estimate its genetic diversity. The clusteranalysis indicated heterogeneity between populations; however, Ocaña individuals were genetically more different, when compared with other populations. A multiple correspondence analysis revealed 2 population groups: the first one including individuals from Ocaña, and the second one that includes individuals from Salazar, Chinácota, Pamplonita and Toledo. This last group showed a higher degree of genetic diversity. We found an average heterozygosity (He) of 0.34 and a fixation index (FST) of 0.13 among populations. These results are likely due to the relatively high genetic distance, observed between Ocaña and the other populations, and because of the effect of geographical barriers in the area. This is the first study in population genetics of this important native timber resource in the northern Andes, and provides relevant information for future conservation strategies and its sustainable use.
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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 | 0.000 |
| 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".