Concept for gene conservation strategy for the endangered Chinese yellowhorn, Xanthoceras sorbifolium, based on simulation of pairwise kinship coefficients
Bibliographic record
Abstract
A molecular markers-based conservation strategy framework for the endemic endangered Chinese yellowhorn, Xanthoceras sorbifolium, is developed. The method utilizes pairwise kinship coefficients, a measure of genetic similarity/dissimilarity, between individuals and contrasts two simulated mating schemes that either promote “no co-ancestry” or “co-ancestry” through multiple matings of particular individuals as well as a “hybrid” scenario involving both “no co-ancestry” and “co-ancestry” mating schemes. Selection of mating individuals is focused on maximizing genetic dissimilarity, hence capturing maximum genetic diversity. These approaches were compared on a population of 79 ancient yellowhorn trees scattered across 13 populations. Simulated matings were evaluated based on their average pairwise kinship, effective population size, and average number of alleles/locus. In this regard, no co-ancestry has been proven to be superior to co-ancestry mating scheme; however, co-ancestry offered opportunities for increasing effective population size, a desirable attribute for combating the detrimental effect of genetic drift. The hybrid approach, with reduced number of crosses, produced acceptable condition with maximum genetic diversity and high effective population size and genetic dissimilarity among the produced offspring. The advantages and disadvantages of marker-based gene conservation are highlighted and discussed. Subject area: Conservation genetics and biodiversity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".