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Record W2165722259 · doi:10.1101/pdb.top066050

Ca<sup>2+</sup>-Sensitive Fluorescent Dyes and Intracellular Ca<sup>2+</sup> Imaging

2013· article· en· W2165722259 on OpenAlexafffund
Martin D. Bootman, Katja Rietdorf, Tony Collins, Simon Walker, Michael J. Sanderson

Bibliographic record

VenueCold Spring Harbor Protocols · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsMcMaster University
FundersBiotechnology and Biological Sciences Research CouncilDirectorate for Biological SciencesNational Institutes of HealthNational Heart, Lung, and Blood InstituteMinistero dello Sviluppo EconomicoOntario Ministry of Economic Development and Innovation
KeywordsFluorescenceBiophysicsIntracellularBrightnessChemistryPhysicsBiologyBiochemistryOptics

Abstract

fetched live from OpenAlex

Imaging Ca(2+)-sensitive fluorescent indicators provides a common approach for studying Ca(2+) signals in many contexts. Fluorescent indicators are particularly useful for measuring acute Ca(2+) changes in a relatively noninvasive manner. The availability of indicators that can be targeted to specific cellular domains, coupled with variations in affinity, brightness or spectral characteristics, provides tools for exploring spatially and temporally diverse Ca(2+) signals, and moreover, multiplexing the readout of Ca(2+) with other cellular functions. This article aims to give the novice experimentalist some insight into the considerations and potential pitfalls that impinge on the use of fluorescent Ca(2+) indicators.

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)0.002
metaresearch head score (Gemma)0.002
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: Protocol · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.308
Teacher spread0.278 · 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
GenreProtocol

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

Citations129
Published2013
Admission routes2
Has abstractyes

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