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Record W2222145113

Captive and free-ranging sea star disease findings from the Seattle, Washington, waterfront during the 2013 sea star ‘wasting disease’ unusual mortality event

2014· article· en· W2222145113 on OpenAlexaboutno aff
Lesanna L. Lahner, Martin Haulena, Michael Garner, C. Drew Harvell, Ian Hewson, Tim Carpenter, Jeff Christiansen

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsnot available
Fundersnot available
KeywordsStar (game theory)WastingDiseaseEvent (particle physics)GeographyHistoryFisheryMedicineBiologyPathology
DOInot available

Abstract

fetched live from OpenAlex

Sea star mortality in several genera including Pycnopodia and Pisaster was unusually high along the west coast of the United States, September-current (December) 2013. Captive and free-ranging animals were analyzed for signs of disease using a variety of diagnostics including cytology, microbiology, histopathology, and transmission electron microscopy. Mortality in regions of the Seattle waterfront and in the captive collection of Pycnopodia maintained at the Seattle Aquarium (n=48) was 100%. Disease was initially observed only in Pycnopodia species (specifically the Sunflower sea star) and over the period of ~ 1 month included a variety of other sea stars including Pisaster and Evasterias. Mortality rates in affected regions of the Salish sea continues to be high (December, 2013). Multiple organizations and collaborators (Cornell University, Wildlife Conservation Society, SeaDoc Society, Monterey Bay Aquarium, Vancouver Aquarium, USGS National Wildlife Health Center, and NW ZooPath) are participating in the ongoing efforts to determine the cause of this unusual mortality event in sea stars. The results to-date of the disease investigation by the Seattle Aquarium and others will be presented.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.003
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 head0.011
GPT teacher head0.225
Teacher spread0.213 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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