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Record W1953163147 · doi:10.1139/cjfas-2013-0050

Complex etiologies of emerging diseases in lobsters (<i>Homarus americanus</i>) from Long Island Sound

2013· article· en· W1953163147 on OpenAlexvenueno aff
Jeffrey D. Shields

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicInvertebrate Immune Response Mechanisms
Canadian institutionsnot available
FundersDivision of Ocean Sciences
KeywordsHomarusEpizooticBiologyPopulationEcologyEtiologyZoologyOutbreakMedicineEnvironmental healthCrustaceanInternal medicine

Abstract

fetched live from OpenAlex

Several diseases have recently emerged in lobsters (Homarus americanus) from Long Island Sound (LIS). Various stressors have been implicated as contributory factors, including increased bottom temperatures, extensive eutrophication with commensurate hypoxia, storm-induced thermal destratification, possible exposures to pesticides and metals, and fishery-induced stressors. Such stressors increase host susceptibility by weakening the host immune defenses and act to increase the transmission and severity of pathogens. The lobster mortality in western LIS in 1999 was linked to Neoparamoeba pemaquidensis, but a complex of stressors resulted in outright mortality from hypoxia or consequent immune suppression that increased susceptibility to the ameba. Similar stressors have been implicated in the etiology of epizootic shell disease and calcinosis. The role of environmental stressors has been hard to delineate, but recent declines in landings indicate that epizootic shell disease has had a negative impact on the lobster population in LIS. Calcinosis, blindness, and hepatopancreatitis are indicators of continued exposure to anthropogenic stressors, but their etiologies remain undetermined. More research is needed to understand emerging diseases, their complex etiologies, and their effects on the lobster population.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.995

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.223
Teacher spread0.202 · 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

Citations50
Published2013
Admission routes1
Has abstractyes

Explore more

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