Frequency‐Dependent Inbreeding Depression in <i>Amsinckia</i>
Bibliographic record
Abstract
If the competitive ability of plants produced by self-pollination differs from that of plants derived by outcrossing, then the magnitude of inbreeding depression may be influenced by the composition of the competitive environment (i.e., the frequency of plants that have arisen from selfing and outcrossing in the neighborhood of "target" plants in which inbreeding depression is expressed). Here, we report the results of experiments designed to examine whether inbreeding depression is influenced by the frequency of inbred plants in the competitive neighborhood. Two species of the annual plant genus Amsinckia were studied, one a near-complete selfer (Amsinckia gloriosa) and the other a partial outcrosser (Amsinckia douglasiana). Competition experiments were conducted in artificial stands composed of different mixtures of inbred and outbred progeny. The fitnesses of progeny were found to be significantly influenced by the composition of the competing neighborhood. The fitness of target plants, however, did not vary monotonically with the frequency of inbred plants in the neighborhood. Rather, for A. gloriosa, maximum performance was observed when there was an intermediate frequency of inbred neighbors. For A. douglasiana, the opposite pattern was found. The results suggest that competition among progeny has the potential to play a role in the selection of self-fertilization and possibly in the maintenance of mixed mating systems.
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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 | 0.000 | 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) | 0.001 | 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".