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Record W6936081637 · doi:10.57494/jjvr.71.3_109

Evaluation of the biofilm detection capacity of the Congo Red Agar method for bovine mastitis-causing bacteria

2024· other· en· W6936081637 on OpenAlexaboutno aff

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsBiofilmBacteriaCongo redAgarIsolation (microbiology)Agar plate

Abstract

fetched live from OpenAlex

The Congo Red Agar method (CRAM), a method for detecting the presence of bacterial biofilm-forming capacity, does not provide sufficient knowledge on the criteria for each bacterial species. In this study, the biofilm detection capacity of the CRAM and the criteria for determining the presence of biofilm-forming capacity of bovine mastitis-causing bacteria were examined. 149 strains isolated from the milk of dairy cows with clinical mastitis were determined for biofilm-forming capacity using the CRAM. The Calgary Biofilm Device Method was also used as a comparative experiment. The study showed that the suitable medium and incubation time in the CRAM differed for each bacterial species, and the criteria for determining the presence of biofilm-forming capacity in each species could be determined.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0090.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.003

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 head0.060
GPT teacher head0.325
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2024
Admission routes1
Has abstractyes

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Same venueInstitutional Repositories DataBase (IRDB)French-language works237,207