Ocean acidification and bivalve byssus: explaining variable responses using meta-analysis
Bibliographic record
Abstract
Numerous studies have documented weakened byssal attachment strength under ocean acidification (OA); however, a comparable number have reported no effect, even within the same species. We used meta-analysis to explore factors that could potentially explain observed effect size variation in byssal attachment strength following OA exposure. A systematic literature search uncovered 20 studies experimentally testing the impact of OA on byssal attachment strength (or some proxy thereof). Meta-analysis revealed body size (mean shell length) to be the strongest predictor of effect size variation, with a negative linear relationship observed between body size and effect size. Despite this relationship, no single study or experiment included body size as a moderating factor. Our finding that the byssal strength of larger bivalves is more susceptible to negative OA effects runs counter to prevailing wisdom that larger, older animals of a given species are more robust to OA than earlier life history stages. This result highlights that body size may be an important factor in moderating OA sensitivity in adult calcifiers; however, in stress response studies, variation in animal size is typically standardized methodologically rather than embraced and warrants further investigation for other taxa and biological traits. In addition to body size, our analysis also identified a suite of previously unconsidered factors that could influence byssal thread attachment strength, including aspects of thread morphology, animal nutritional status, and the impact of seasonality, which we highlight to provide suggestions for future research.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.030 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".