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Record W4200085945 · doi:10.21775/cimb.042.385

Live Imaging

2020· review· en· W4200085945 on OpenAlexafffund
George Chaconas, Tara J. Moriarty, Jon T. Skare, Jenny A. Hyde

Bibliographic record

VenueCurrent Issues in Molecular Biology · 2020
Typereview
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsUniversity of TorontoUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health Research
KeywordsLive cell imagingComputer scienceCover (algebra)Artificial intelligenceComputer visionBiologyEngineeringCell

Abstract

fetched live from OpenAlex

Being able to vizualize a pathogen at a site of interaction with a host is an aesthetically appealing idea and the resulting images can be both informative as well as enjoyable to view. Moreover, the approaches used to derive these images can be powerful in terms of offering data unobtainable by other methods. In this article, we review three primary modalities for live imaging Borrelia spirochetes: whole animal imaging, intravital microscopy and live cell imaging. Each method has strengths and weaknesses, which we review, as well as specific purposes for which they are optimally utilized. Live imaging borriliae is a relatively recent development and there was a need of a review to cover the area. Here, in addition to the methods themselves, we also review areas of spirochete biology that have been significantly impacted by live imaging and present a collection of images associated with the forward motion in the field driven by imaging studies.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.027
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0270.021

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 head0.024
GPT teacher head0.365
Teacher spread0.341 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2020
Admission routes2
Has abstractyes

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Same venueCurrent Issues in Molecular BiologySame topicVector-borne infectious diseasesFrench-language works237,207