Bibliographic record
Abstract
The opportunity for selection, I, defined as the variance in relative fitness, has been called an estimate of the ‘total amount of selection’. However, while a non-zero I is a necessary condition for selection, it is not a sufficient one. We investigated the relationship between I and the magnitude of standardized linear and non-linear selection gradients for body size in the waterstrider Aquarius remigis, measured over three episodes of selection. Male I exceeded female I for daily reproductive success, but the difference was not statistically significant and had little impact on net adult I. Linear selection gradients were only weakly correlated with I, while non-linear gradients were uncorrelated with I. Partitioning I among the three episodes of selection revealed that variance in net adult fitness was largely generated by variance in prereproductive survival. The patterns of selection across the adult life stage suggested by analysis of the opportunity for selection differed qualitatively and quantitatively from those revealed by selection gradient analysis. In particular, the former identified pre-reproductive survival as the key component of net adult fitness, even though there is little selection on total length in this life stage. We conclude that I is a useful adjunct to selection gradient analyses, but is perhaps most useful in the analysis of life-history evolution where the traits themselves are direct estimates of fitness.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".