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Record W3164644311 · doi:10.1080/08927014.2021.1917555

An automated image analysis platform for the study of weakly -adhered cells

2021· article· en· W3164644311 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiofouling · 2021
Typearticle
Languageen
FieldEngineering
TopicMarine Biology and Environmental Chemistry
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDragMaterials scienceVolumetric flow rateSoftwareAdhesionNanotechnologyComputer scienceBiological systemComposite materialBiologyMechanicsPhysics

Abstract

fetched live from OpenAlex

Details of the design and implementation of an open-source platform for studying the adhesion of cells attached to solid substrata are provided. The hardware is based on a laser-cut flow channel connected to a programmable syringe pump. The software automates all aspects of the flow rate profile, data acquisition and image analysis. An example of the pelagic diatom Thalassiosira rotula adhered to poly(dimethyl siloxane) surfaces is provided. The procedure described enables the shear rate to be converted to drag force for arbitrary-shaped objects, of utility to the study of many cell species, especially ones that are obviously non-spherical. It was determined that 90% of cells are removed with the application of drag forces < 3×10−12 N, and that this value is relatively independent of the incubation time on the surface. This result is important to understand how marine species interact with polymer surfaces that are used in electrical insulator 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.

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 score0.454
Threshold uncertainty score0.265

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 head0.245
Teacher spread0.234 · 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