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Record W4381939375 · doi:10.1093/icesjms/fsad059

Evidence for an effective defence against ocean acidification in the key bioindicator pteropod <i>Limacina helicina</i>

2023· article· en· W4381939375 on OpenAlexafffund
M.R. Miller, Rosie L. Oakes, Paul A. Covert, Debby Ianson, John F. Dower

Bibliographic record

VenueICES Journal of Marine Science · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsFisheries and Oceans CanadaUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOcean acidificationBioindicatorDissolutionEcosystemEnvironmental scienceOceanographyMarine ecosystemEnvironmental chemistryEcologyChemistryGeologyBiologySeawater

Abstract

fetched live from OpenAlex

Abstract The pteropod Limacina helicina has become an important bioindicator species for the negative impacts of ocean acidification (OA) on marine ecosystems. However, pteropods diversified during earlier high CO2 periods in Earth history and currently inhabit regions that are naturally corrosive to their shells, suggesting that they possess mechanisms to survive unfavourable conditions. Recent work, which is still under considerable debate, has proposed that the periostracum, a thin organic coating on the outer shell, protects pteropods from shell dissolution. Here, we provide direct evidence that shows that damage to the L. helicina periostracum results in dissolution of the underlying shell when exposed to corrosive water for ∼8 d, while an intact periostracum protects the shell from dissolution under the same conditions. This important first line of defence suggests that pteropods are more resistant to OA-induced shell dissolution than is generally accepted.

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)0.007
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 score0.455
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.051
GPT teacher head0.312
Teacher spread0.261 · 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

Citations14
Published2023
Admission routes2
Has abstractyes

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