Assessment of genetic diversity and differentiation of<i>Elymus nutans</i>indigenous to Qinghai–Tibet Plateau using simple sequence repeats markers
Bibliographic record
Abstract
Chen, S., Zhang, X., Ma, X. and Huang, L. 2013. Assessment of genetic diversity and differentiation of Elymus nutans indigenous to Qinghai–Tibet Plateau using simple sequence repeats markers. Can. J. Plant Sci. 93: 1089–1096. Elymus nutans Griseb., an important alpine forage grass, is widely distributed in the Qinghai–Tibet Plateau. A total of 50 E. nutans accessions from the eastern Qinghai–Tibet Plateau were analyzed using simple sequence repeats (SSR) markers from wheat and Elymus species. Our results show that a total of 144 reliable bands were generated, of which 132 (91.38%) were found to be polymorphic. Nei-Li's genetic similarity coefficients ranged from 0.515 to 0.870 with an average of 0.719, which shows a high level of genetic diversity and a broad genetic base among accessions. There was a low correlation between genetic distance and geographical distance (r=0.121, P=0.088) in the region, which is consistent with the unweighted pair group method with arithmetic average cluster analysis of accessions. The mountain ridges and river valleys in the eastern Qinghai–Tibet region could serve as genetic barriers for pollinator movement and seed dispersal. The rule of the most genetic diversity at medium altitude of E. nutans in the Qinghai–Tibet Plateau was also validated in the study. The implications of these results for the conservation of E. nutans are discussed.
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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 | 0.001 | 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".