Sensitivity of sea urchin fertilization to pH varies across a natural pH mosaic
Bibliographic record
Abstract
Abstract In the coastal ocean, temporal fluctuations in pH vary dramatically across biogeographic ranges. How such spatial differences in pH variability regimes might shape ocean acidification resistance in marine species remains unknown. We assessed the pH sensitivity of the sea urchin Strongylocentrotus purpuratus in the context of ocean pH variability. Using unique male–female pairs, originating from three sites with similar mean pH but different variability and frequency of low pH (pHT ≤ 7.8) exposures, fertilization was tested across a range of pH (pHT 7.61–8.03) and sperm concentrations. High fertilization success was maintained at low pH via a slight right shift in the fertilization function across sperm concentration. This pH effect differed by site. Urchins from the site with the narrowest pH variability regime exhibited the greatest pH sensitivity. At this site, mechanistic fertilization dynamics models support a decrease in sperm–egg interaction rate with decreasing pH. The site differences in pH sensitivity build upon recent evidence of local pH adaptation in S. purpuratus and highlight the need to incorporate environmental variability in the study of global change biology.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".