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Record W2286442349 · doi:10.1007/s10974-015-9437-x

Insight into muscle physiology through understanding mechanisms of muscle pathology

2015· editorial· en· W2286442349 on OpenAlexaboutno aff
Maria Jolanta Rędowicz, Joanna Moraczewska

Bibliographic record

VenueJournal of Muscle Research and Cell Motility · 2015
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsnot available
FundersUniversity of ThessalyUniversität Salzburg
KeywordsPhysiologyBiologyProteomicsPathologyAnatomyMedicineBiochemistry

Abstract

fetched live from OpenAlex

The main theme of the 44th EMC was Muscle Research in Health and Disease. It was the intention of the organizers to cover a wide range of topics focusing on muscle development and function, both in physiology and pathology. The program included the following sessions: ''Molecular motors'', ''Acto-myosin interactions'', ''Muscle cytoskeleton'', ''Muscle development and repair'', ''Neuro-muscular interactions'', ''Excitation-contraction coupling'', ''Skeletal muscle diseases'', ''Heart and heart failure'', ''Smooth muscle in health and disease'', ''Muscle metabolism and bioenergetics'' and ''Muscle exercise and plasticity''. The sessions were chaired by top scientists in these fields, including Polish muscle researchers. The organizers devoted two sessions to memorize outstanding muscle scientists who recently passed away. The session on ''Acto-myosin interactions'' was devoted to the memory of Professor Andrew Szent-Gyo rgy, and the session on ''Smooth muscle in health and disease'' was to the memory of Professor Renata Da browska. The Young Scientist Session which preceded the Conference opening ceremony consisted of two parts: (i) How to stay motivated in science and (ii) Muscle biology: from genes to muscle. The opening lecture was presented by Professor Michael Rudnicki from Ottawa University, who pioneered studies elucidating the molecular basis of muscle development.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.002
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 head0.054
GPT teacher head0.336
Teacher spread0.283 · 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.

Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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