MétaCan
Menu
Back to cohort
Record W3093655756 · doi:10.1038/s41522-020-00153-9

Biofilm dynamics: linking in situ biofilm biomass and metabolic activity measurements in real-time under continuous flow conditions

2020· article· en· W3093655756 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenpj Biofilms and Microbiomes · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBiofilmModular designBiomass (ecology)Biological systemEnvironmental scienceFunction (biology)BiocideComputer scienceMetabolic activityProcess engineeringBiochemical engineeringChemistryEcologyBiologyEngineeringBacteria

Abstract

fetched live from OpenAlex

The tools used to study biofilms generally involve either destructive, end-point analyses or periodic measurements. The advent of the internet of things (IoT) era allows circumvention of these limitations. Here we introduce and detail the development of the BioSpec; a modular, nondestructive, real-time monitoring system, which accurately and reliably track changes in biofilm biomass over time. The performance of the system was validated using a commercial spectrophotometer and produced comparable results for variations in planktonic and sessile biomass. BioSpec was combined with the previously developed carbon dioxide evolution measurement system (CEMS) to allow simultaneous measurement of biofilm biomass and metabolic activity and revealed a differential response of these interrelated parameters to changing environmental conditions. The application of this system can facilitate a greater understanding of biofilm mass-function relationships and aid in the development of biofilm control strategies.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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