Bibliographic record
Abstract
Genetic monitoring, the quantification of temporal changes in population genetics and dynamics metrics generated by using appropriate parameters, constitutes a method with a prognostic value. Genetic monitoring has been recognized in several international agreements and documents, and can be an important tool for the protection of biodiversity. However, approaches developed so far for perennial plant species are rather cumbersome for practical use. It is proposed that perennial plant genetic monitoring should focus on keystone species of biological and economical importance, as well as rare or endangered species. In addition, genetic monitoring should concentrate on gene conservation units of such species, to be advanced in a dynamic gene conservation scheme. Three indicators are proposed for genetic monitoring based on a gene-ecological approach: natural selection, genetic drift, and a gene flow-mating system. These are evaluated based on three demographic (age and size class distribution, reproductive fitness, regeneration abundance) and four genetic (effective population size, allelic richness, latent genetic potential, outcrossing/actual inbreeding rate) parameters. Minimum sample sizes, critical levels of differences among parameters, and costs for temporal evaluation are proposed. The benefits of the immediate application of genetic monitoring are highlighted.
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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 | 0.002 | 0.003 |
| 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".