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Record W2900798894 · doi:10.1656/045.025.0411

A First Report of Shell Disease Impacting Cancer borealis (Jonah Crab) in the Bay of Fundy

2018· article· en· W2900798894 on OpenAlexaboutno aff
David B. Carlon, Patrick Warner, Clay Starr, David J. Anderson, Zakir Bulmer, Hugh Cipparone, Jesse Dunn, Caroline Godfrey, Claire Goffinet, Miranda Miller, Charlotte Nash

Bibliographic record

VenueNortheastern Naturalist · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsnot available
Fundersnot available
KeywordsBayCrustaceanFisheryOutbreakPredationShell (structure)Herring gullBiologyHerringEcologyOceanographyLarusFish <Actinopterygii>Geology

Abstract

fetched live from OpenAlex

Several shell diseases are impacting a variety of decapod crustaceans in southern New England, but have rarely been reported in the colder waters of the eastern Gulf of Maine. Here we document a possible outbreak of shell disease impacting Cancer borealis (Jonah Crab) on Kent Island, NB, Canada. On low tides of 31 August 31–3 September 2017 we found hundreds of Jonah Crabs stranded above the tide line and resting on top of the dense canopies of fucoid algae. Closer inspection of exoskeletons revealed the clinical signs of classical shell disease: dark circular patches and lesions that penetrated the cuticle. A sample of 30 stranded Jonah Crabs revealed that 28 (93%) were adult females. On the next low tide, we found the same pattern of exposed Jonah Crabs and observed numerous instances of Larus smithsonianus (Herring Gull) predation. Continuous monitoring of shallow-water temperatures over the last 3 years revealed that average daily summer temperatures have been regularly exceeding a shell-disease threshold of 12 °C on Kent Island. Between 13 September and 31 October 2015 there were 19 days with an average water temperature above 12 °C and 43 days during the same interval in 2016.

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)0.000
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 score0.159
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.272
Teacher spread0.256 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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