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Record W2047784262 · doi:10.1093/bib/bbm018

SenseLab: new developments in disseminating neuroscience information

2007· article· en· W2047784262 on OpenAlex
Chiquito Crasto, Luis Marenco, Nan Liu, Thomas M. Morse, Kei‐Hoi Cheung, Peter Lai, Gautam Bahl, Peter Masiar, Hugo Y. K. Lam, Ee Jean Lim, Huai Chen, P. Nadkarni, Michele Migliore, Perry L. Miller, G. M. Shepherd

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.

Bibliographic record

VenueBriefings in Bioinformatics · 2007
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsVictoria Park
FundersU.S. National Library of MedicineNational Institute of Neurological Disorders and StrokeNational Institute on Deafness and Other Communication DisordersNational Human Genome Research InstituteNational Institute on Drug AbuseRaymond and Beverly Sackler Institute for Biological, Physical and Engineering Sciences, Yale UniversityYale UniversityNational Institutes of HealthNational Science Foundation
KeywordsNeuroinformaticsInteroperationDisseminationComputer scienceSuiteNeuroscienceGenomicsSystems neuroscienceData scienceWorld Wide WebInteroperabilityBiologyGenomeGene

Abstract

fetched live from OpenAlex

This article presents the latest developments in neuroscience information dissemination through the SenseLab suite of databases: NeuronDB, CellPropDB, ORDB, OdorDB, OdorMapDB, ModelDB and BrainPharm. These databases include information related to: (i) neuronal membrane properties and neuronal models, and (ii) genetics, genomics, proteomics and imaging studies of the olfactory system. We describe here: the new features for each database, the evolution of SenseLab's unifying database architecture and instances of SenseLab database interoperation with other neuroscience online resources.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.085
GPT teacher head0.280
Teacher spread0.195 · 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