Eco-evolutionary dynamics of intergenomic epistatic QTLs under slight and hard multilevel selections in community genetics
Bibliographic record
Abstract
Eco-evolutionary community genetics refers to the study of population genetics at multispecies levels since a single species evolves genetically through interactions with other species. The aim of this paper is dynamical modeling of interspecific quantitative trait loci (QTL) under slight and hard multilevel selection to investigate eco-evolutionary genetic relationships among QTL of multispecies. It was found that the simplex explained the intergenomic epistatic QTL (IEQ) dynamics in a discrete-time model according to Wright’s manifold. Besides, the generalized gradient system on the simplex demonstrated the IEQ dynamics in a continuous-time model. The outcomes of slight and hard multilevel selection on the expressions of IEQ in species were explained by the gradient operator of the mean multilevel fitness. IEQ analysis was integrated into community genetics according to Lande’s assumptions, so for slight multilevel selection, the geometric average of the mean multilevel fitness specified Wright’s idea of adaptive topography. The dynamical system for phenotypic eco-evolution under a hard multilevel selection gradient was studied and the relative mean multilevel fitness of two species was estimated.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".