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Record W4399204197 · doi:10.1038/s42003-024-06379-z

Centroid of the bacterial growth curves: a metric to assess phage efficiency

2024· article· en· W4399204197 on OpenAlexafffund
Nava Hosseini, Mahdi Chehreghani, Sylvain Moineau, Steve J. Charette

Bibliographic record

VenueCommunications Biology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecMcGill UniversityUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsCentroidMetric (unit)Index (typography)Selection (genetic algorithm)Replication (statistics)Biological systemBacteriophageStatisticsMathematicsComputer scienceData miningBiologyArtificial intelligenceGeometryEngineeringGeneticsEscherichia coliGene

Abstract

fetched live from OpenAlex

Phage replication can be studied using various approaches, including measuring the optical density (OD) of a bacterial culture in a liquid medium in the presence of phages. A few quantitative methods are available to measure and compare the efficiency of phages by using a single index based on the analysis of OD curves. However, these methods are not always applicable to non-canonical OD curves. Using the concept of center of area (centroid), we developed a metric called Centroid Index (CI), sensitive to the trend of the growth curves (OD distribution) including bacterial regrowth, which is not considered by the methods already available. We also provide a user-friendly software to facilitate the calculation of CI. This method offers an alternative and more precise way to determine phage efficiency by considering the OD variations over time, which may help in the selection of phages for biocontrol applications.

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)0.012
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.035
GPT teacher head0.316
Teacher spread0.281 · 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

Citations15
Published2024
Admission routes2
Has abstractyes

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