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Record W4385604929 · doi:10.1016/j.lwt.2023.115136

Changes in bacterial communities in chilled American lobster (Homarus americanus) tissues following mortality

2023· article· en· W4385604929 on OpenAlexaff
Jonathan Rolin, Ryan A. Horricks, Kiersten Watson, K. Fraser Clark, Leah M. Lewis‐McCrea, G. K. Reid

Bibliographic record

VenueLWT · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsDalhousie UniversityAgriculture and Agri-Food Canada
Fundersnot available
KeywordsHomarusBiologyAmerican lobsterFood spoilageZoologyPhotobacterium phosphoreumClawFisheryBacteriaEcologyCrustacean

Abstract

fetched live from OpenAlex

Studies evaluating bacteria growing on dead American lobster (Homarus americanus) are typically based on a single observation of the post-mortem period. In this study, seventy-five lobsters were euthanized to evaluate changes in microbiological composition between sexes, and in gut, tail, and claw tissues over a 4-day period under chilled (4 °C) storage. The hygienic quality of meat decreased over the storage period but remained acceptable (<6 log CFU/g). Metagenomic analysis showed alpha and beta diversity differed by tissue type throughout storage time. Photobacterium spp. Were the primary spoilage bacteria identified in gut (49%) and tail (89%) but were less abundant in the claw (11%). Photobacterium spp. Abundance were unchanged in the gut over the sampling period, whereas their proportion increased in the tail (62%) and claw (32%) over the sampling period. Unlike other studies, Photobacterium phosphoreum was the predominant species detected. The detection of bacteria not previously reported in American lobster suggest there could be an association between the microbiota in lobster tissues with their diet and/or habitat, and these factors could influence the spoilage bacteria inhabiting these tissues. Findings identify an opportunity to recover tail and claw meat from dead lobsters with a quality acceptable for human consumption.

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 score0.367
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.276
Teacher spread0.255 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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