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Record W1971219483 · doi:10.1177/108201320000600209

Note. Growth of Vibrio parahaemolyticus in lobster homogenates at different temperatures / Nota. Crecimiento de Vibrio parahaemolyticus en langosta a diferentes temperaturas

2000· article· en· W1971219483 on OpenAlexaff
T. Ferreira Magalhães, Regine Helena Silva dos Fernandes Vieira, S.H. Ferreira Façanha, Ernesto Hofer, A.M. Martin

Bibliographic record

VenueFood Science and Technology International · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsVibrio parahaemolyticusVibrioIncubationMicrobiologyBiologyBacteriaCell countingColony-forming unitFood scienceChemistryCellBiochemistry

Abstract

fetched live from OpenAlex

Due to the illnesses associated with the presence of Vibrio parahaemolyticus in seafoods, its survival in lobster was studied. Lobster homogenates were prepared, sterilized, inoculated with the bacteria, and incubated at -25 °C, 6 °C and 28 °C, for 60 days. Different viable cell counting methods were employed. The results of the McFarland nephelometric standard method and the direct count of viable bacterial cells, (DVC) were not significantly different (p = 0.5). However, the DVC method was better for detecting viable cells at low numbers for longer times, especially for cultures incubated at -25 °C. Therefore, this method, and the plate count (PC) method for counting colony forming units, were selected for monitoring the survival of V. parahaemolyticus under each temperature of incuba tion. The greatest survival of the bacterium was at ambient (28 °C) temperature. At freezer tempera ture (-25 °C), no viable cells were detected towards the end of the experiments. However, the cultures kept at 6 °C were viable until the end of the incubation period, these results being different from previously published works conducted at refrigeration temperatures.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
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 head0.006
GPT teacher head0.256
Teacher spread0.250 · 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 designBench or experimental
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

Citations15
Published2000
Admission routes1
Has abstractyes

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