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Record W2162189059 · doi:10.1109/isot.2010.5687357

Robotic platform for real-time tracking of a single fast swimming bacterium

2010· article· en· W2162189059 on OpenAlexafffund
Charles C. Tremblay, Joscelyn Jean, Laurence Marchand, Ali Turki, Philippe Chouinard-Gaouette, Mathieu Brousseau, Mahmood Mohammadi, Sylvain Martel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTracking (education)Computer sciencePixelFrame rateSegmentationArtificial intelligenceFrame (networking)Computer visionSoftwareLock (firearm)Image segmentationHardware architectureComputer hardwareEngineering

Abstract

fetched live from OpenAlex

In this paper we present a hardware architecture with software implementation able to track free swimming single 2μm in diameter MC-1 bacterium. The computer vision system operates at up to 77 fps at full speed and up to 24 fps when recording full 512×512 pixels frame from coupled-charge device (CCD) array. Closed-loop control with lock-in tracking is achieved using the Otsu Segmentation Method (OSM) with a cubic spline model-based predictive algorithm. Using the system, speed distribution of MC-1 cells has been recorded showing a m ean speed of 200μm/s. Tracking is demonstrated over a range of a few millimeters during 30 sec.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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

Citations3
Published2010
Admission routes2
Has abstractyes

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