Effect of habitat fragmentation on the genetic diversity of <i>Stipa krylovii</i> Reshov. in an agro-pastoral ecotone in northern China
Bibliographic record
Abstract
Native grassland in China have been fragmented due to the introduction of agriculture, which has the potential to reduce genetic diversity. In order to understand the potential effects of fragmentation, we conducted a study to examine the genetic diversity between two populations of Stipa krylovii in a typical steppe ecoregion, in northern China. One population was fragmented by farmland (PF) while the second was continuous steppe (PS). The populations were 30 km apart. Genetic diversity was assessed by sampling plants in four geographically similar subpopulations in each population and analyzed for their DNA using the inter-simple sequence repeats (ISSR) markers. Of 50 primers tested, 7 produced 122 amplified bands from 120 individuals, of which 92% (112) were polymorphic. According to the UPGMA dendogram, the four PF subpopulations were grouped separately from the four PS subpopulations. However, AMOVA analysis indicated that habitat fragmentation over the past 50 yr had not changed genetic diversity and variation among S. krylovii populations in an agro-pastoral ecotone in northern China. Therefore, the genetic diversity of this species can be maintained if agriculture disturbance is not increased. Key words: Genetic variation, gene flow, ISSR, fragmented population, non-fragmented population
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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) | 0.000 | 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".