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Record W4388365601 · doi:10.1016/j.ibneur.2023.08.638

A NOVEL DATA ANALYSIS PIPELINE FOR FIBER-BASED IN VIVO CALCIUM IMAGING

2023· article· en· W4388365601 on OpenAlexaff
Catherine Thomas, Du Xuejun, Kai Wang, Jayant Rai, Ken-ichi Okamoto, Miles Q. Li, Jian Zhao

Bibliographic record

VenueIBRO Neuroscience Reports · 2023
Typearticle
Languageen
FieldNeuroscience
TopicBrain Tumor Detection and Classification
Canadian institutionsAmgen (Canada)University of TorontoUniversity of WaterlooToronto East General Hospital
Fundersnot available
KeywordsPipeline (software)Computer scienceCalciumIn vivoData scienceMedicineBiologyInternal medicine

Abstract

fetched live from OpenAlex

Examining in vivo neural circuit dynamics in relation to behaviour is crucial to advances in understanding how the brain works. Two techniques that are often used to examine these dynamics are one photon calcium imaging and optogenetics. Fiber-based micro-endoscopy provides a versatile, modular, and lightweight option for combining in vivo calcium imaging and optogenetics in freely behaving animals. One challenge with this technique is that the data collected from such an approach are often complex and dense.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0190.018

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 head0.147
GPT teacher head0.361
Teacher spread0.215 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2023
Admission routes1
Has abstractyes

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