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Record W2000087379 · doi:10.4319/lo.2010.55.6.2331

Killer storms: North Atlantic hurricanes and disease outbreaks in sea urchins

2010· article· en· W2000087379 on OpenAlexafffundabout
Robert E. Scheibling, Jean‐Sébastien Lauzon‐Guay

Bibliographic record

VenueLimnology and Oceanography · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsFisheries and Oceans CanadaDalhousie University
FundersFisheries and Oceans Canada
KeywordsOceanographySea urchinKelpOutbreakStormEnvironmental scienceClimate changeSea surface temperatureTropical cycloneKelp forestMarine ecosystemClimatologyFisheryGeographyEcosystemEcologyBiologyGeology

Abstract

fetched live from OpenAlex

An increase in the incidence of disease in various marine organisms over the past few decades has been linked to ocean climate change. In Nova Scotia, Canada, mass mortalities of sea urchins, due to an amoebic disease, are associated with tropical cyclones of relatively high intensity that pass close to the coast when water temperature is above a threshold for disease propagation. These conditions increase the likelihood of introduction and spread of a nonindigenous water‐borne pathogen through turbulent mixing. Our analysis shows that the most deadly storms, in terms of the probability of a sea urchin mass mortality, have become more deadly over the past 30 years. We also found that storms have been tracking closer to the coast and that surface temperature has increased during the hurricane season. These trends are likely to continue with climate warming, resulting in a regional shift to a kelp bed ecosystem and the loss of the urchin fishery.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

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

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 head0.003
GPT teacher head0.180
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), 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

Citations59
Published2010
Admission routes3
Has abstractyes

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