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Record W6993003142

Modifiability of residual force depression in single muscle fibres following uphill and downhill training in rats

2020· dissertation· en· W6993003142 on OpenAlexfundno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2020
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Biological Computing
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIsometric exerciseMuscle contractionMuscle fatigueContraction (grammar)Depression (economics)Work (physics)Muscle stiffnessResidual
DOInot available

Abstract

fetched live from OpenAlex

Following active muscle shortening, steady-state isometric force is less than a purely isometric contraction at the same muscle length and level of activation; this is known as residual force depression (rFD). The purpose of this thesis was to investigate whether rFD in single muscle fibres is modifiable through downhill and uphill running in the extensor digitorum longus (EDL) and soleus (SOL) muscles in rats. Rats were randomly assigned to either a control or intervention group, where they were run uphill or downhill 5 days/wk for 4 wks. rFD was present for both the EDL (6-15%) and SOL (1-2%), with no effect of training. rFD was greater for the EDL than SOL which closely corresponded to the greater work of shortening and stiffness depression in the EDL. These results indicate that while rFD was observed, training did not appear to alter this intrinsic history-dependent property of single muscle fibres.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.230
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

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