MétaCan
Menu
Back to cohort
Record W2737139249

Mechanics of Sarcomeres in Series and Instability - JURA Best Paper Award 2016-2017

2017· article· en· W2737139249 on OpenAlexaffvenue
Jared Collette, Azim Jinha, Walter Herzog

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSarcomereInstabilityMechanicsTitinMyofibrilPhysicsAnatomyChemistryBiologyMyocyte
DOInot available

Abstract

fetched live from OpenAlex

Sarcomeres are the smallest independent unit of force production in the muscle. Current theoretical models of sarcomere in series, i.e. a myofibril, predict instability on the descending limb region of the force-length relationship. However, experimental evidence suggests that sarcomeres can be stable on the descending limb region with non-uniform lengths. The models presented re-evaluates the assumption that sarcomeres are independent units of contraction. Instead, it is hypothesized that there is a dependency between sarcomeres for force generation. Sarcomeres in series were modelled, with force as the dependent variable and sarcomere length and time as the independent variables. Models were developed with both independent and dependent sarcomere force generation. The independent sarcomere models resulted in instability that current theoretical models predict. Two cases of dependent sarcomere models were implemented, both included a shift in the passive force with varying degrees of dependency between adjacent sarcomeres. With these models, there was either stability with non-uniform length, stability with uniform length, or instability on the descending limb region of the force-length relationship. The major finding was that mathematically, sarcomeres with a variable passive force can reach equilibrium at various lengths if a dependency between adjacent sarcomeres is incorporated into the models.

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)0.003
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 score0.413
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.087
GPT teacher head0.391
Teacher spread0.304 · 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 designObservational
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

Citations1
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of undergraduate research in AlbertaSame topicCardiomyopathy and Myosin StudiesFrench-language works237,207