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Record W4285268912 · doi:10.3354/meps14101

Ocean acidification and bivalve byssus: explaining variable responses using meta-analysis

2022· article· en· W4285268912 on OpenAlexaff
Jeff C. Clements, MN George

Bibliographic record

VenueMarine Ecology Progress Series · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsByssusMeta-analysisOcean acidificationEcologyBiologyClimate changeMusselMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.044
GPT teacher head0.264
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2022
Admission routes1
Has abstractyes

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