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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 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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.147
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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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