Phylogeny in Few Species of Leguminosae Family Based on <i>matK</i> Sequence
Bibliographic record
Abstract
In this paper, few species of Leguminosae family considered for phylogenetically analyses which are found in Gujarat state in India and matK gene sequence data from NCBI database are considered for evolutionary analysis. The sequence data of the matK gene are more accurate than rbcL sequence data in the reconstruction of phylogenies throughout the seed plants. Leguminosae family is one of the largest families that contain thousands of species of Plants, Herbs, Shrubs and Trees worldwide. This study shows that species of Leguminosae family which is further classified into Fabaceae (Papilionaceae), Mimosaceae and Caesalpiniaceae; based on morphological characters has different members and the based on the DNA and protein matK sequence data analysis, few species are not related with each other as per morphological classification. We conclude that few species are related with each other as per botanical or morphological classification of Leguminosae family but evolutionary results shows that based on DNA and protein matK sequence data some species are not related with morphological or taxonomical classification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".