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Record W4411345874 · doi:10.1139/bcb-2025-0146

Proceedings of the Canadian Society for Molecular Biosciences (CSMB) 2024 Conference — Gene Regulation: From Cells to Systems

2025· article· en· W4411345874 on OpenAlexafffundvenueabout
Mojgan Rastegar, Hans‐Joachim Wieden, James Davie

Bibliographic record

VenueBiochemistry and Cell Biology · 2025
Typearticle
Languageen
FieldMedicine
TopicScience, Research, and Medicine
Canadian institutionsUniversity of Manitoba
FundersInstitute of Genetics
KeywordsBiologyEpigeneticsTheme (computing)Multidisciplinary approachCell and molecular biologyLibrary sciencePhysiologyGeneticsSociologyGeneSocial science

Abstract

fetched live from OpenAlex

The 67th Canadian Society for Molecular Biosciences (CSMB) Annual Conference took place in Winnipeg, Manitoba, from 6 May to 8 May 2024, at the University of Manitoba, Winnipeg. The conference theme "Gene Regulation: From Cells to Systems" was selected to reflect the breadth and multidisciplinary nature of CSMB's membership and to facilitate interactions among scientists and researchers across different disciplines and with diverse research programs and approaches. This meeting attracted registered participants from provinces across Canada and the USA. The objective of this meeting was to promote the inclusive dissemination, conversation, and discussions of molecular biosciences research, in different areas of molecular biosciences covering a wide range of topics in basic science and health-related research subjects such as neuroscience, cancer biology, immunology, physiology, bacterial systems, RNA biology, protein homeostasis, cellular metabolism and cell death mechanisms, neurobiology and neurogenetics, epigenetics, chromatin biology, environmental influence, stem cell biology, and genome-wide studies. This meeting provided a discussion opportunity for molecular bioscience researchers to develop innovative and novel strategies and collaborations for biomedical and translational research.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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
Published2025
Admission routes4
Has abstractyes

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