A comparative analysis of experimental selection on the stickleback pelvis
Bibliographic record
Abstract
Mechanisms of natural selection can be identified using experimental approaches. However, such experiments often yield nonsignificant effects and imprecise estimates of selection due to low power and small sample sizes. Combining results from multiple experimental studies might produce an aggregate estimate of selection that is more revealing than individual studies. For example, bony pelvic armour varies conspicuously among stickleback populations, and predation by vertebrate and insect predators has been hypothesized to be the main driver of this variation. Yet experimental selection studies testing these hypotheses frequently fail to find a significant effect. We experimentally manipulated length of threespine stickleback (Gasterosteus aculeatus) pelvic spines in a mesocosm experiment to test whether prickly sculpin (Cottus asper), an intraguild predator of stickleback, favours longer spines. The probability of survival was greater for stickleback with unclipped pelvic spines, but this effect was noisy and not significant. We used meta-analysis to combine the results of our mesocosm experiment with previously published experimental studies of selection on pelvic armour. We found evidence that fish predation indeed favours increased pelvic armour, with a moderate effect size. The same approach found little evidence that insect predation favours reduced pelvic armour. The causes of reduced pelvic armour in many stickleback populations remain uncertain.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".