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Record W2997832907 · doi:10.2983/035.038.0326

Life History Traits Conferring Larval Resistance against Ocean Acidification: The Case of Brooding Oysters of the Genus Ostrea

2019· article· en· W2997832907 on OpenAlexaff
Matthew W. Gray, Óscar R. Chaparro, Klaus B. Huebert, Sean O’Neill, Tania Couture, A.H. Moreira, Damian C. Brady

Bibliographic record

VenueJournal of Shellfish Research · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsMount Allison UniversityMcGill University
FundersOffice of Experimental Program to Stimulate Competitive ResearchNational Science Foundation
KeywordsOstrea edulisBiologyOysterContext (archaeology)Resistance (ecology)EcologyOcean acidificationBroodEcosystemFisheryClimate change

Abstract

fetched live from OpenAlex

As oceans and many estuaries become more acidic, identifying adaptable or nonadaptable species (“winners” or “losers”) will enable better predictions of community and ecosystem function alterations due to climate change. Marine bivalves are frequently subjects of ocean acidification (OA) research because of their perceived vulnerability, which also threatens loss of their valuable ecosystem services. Studies indicate that larvae of many broadcast spawning oyster and mussel species are physiologically sensitive to alterations in carbonate chemistry. Running counter to this trend are recent investigations of brooding oyster species (genus Ostrea) that suggest their offspring may be considerably more resistant to OA stress. Although the precise mechanism conferring OA resistance to Ostrea larvae is unknown, a strong candidate appears to be exaptation of traits developing embryos that require to cope with adverse carbonate conditions they typically encounter in the brood chamber. New and previously reported data on Ostrea brood chamber conditions are discussed in the context of OA. Novel technical and experimental approaches are offered to address current knowledge gaps in future studies.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.279
Teacher spread0.215 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations22
Published2019
Admission routes1
Has abstractyes

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