MétaCan
Menu
Back to cohort
Record W4381046492 · doi:10.15173/sciential.v1i10.3517

Sciential Issue 10

2023· article· en· W4381046492 on OpenAlex
Sciential Journal

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSciential - McMaster Undergraduate Science Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

response to different contraction velocities.The results of this experiment will be analysed and discussed.Specifically, we will explore what caused the experimental results and how the literature supports them.Conclusively, this paper will discuss the resistance training regimen most conducive to muscle hypertrophy, incorporating findings from the literature review and experiment. Causes of Muscle Hypertrophy Metabolic StressMetabolic stress following resistance training leads to numerous hypertrophic effects that impact the subcellular structure of myocytes, most notably the accretion of metabolites within the cells.Sufficient training intensity has been shown to elicit fast glycolysis for quick energy generation, in the form of ATP. 1 Lactic acid is released as a by-product of this process, which OPEN ACCESSResistance training is essential to muscle hypertrophy as it fatigues fibres through time-under-tension (TUT).As myocyte energy depletes, metabolites accrete, leading to inflammation to increase cell size so it is adapted for future stimuli.TUT can be measured by varying eccentric velocities: i.e., the rate at which a muscle lengthens under load.A longer period of lengthening will lead to greater metabolite accretion and inflammation.However, it is unknown whether TUT has a threshold or if it can gradually increase and lead to more muscle growth.Through a literature review and an experiment, this project investigates the effect of varying eccentric velocity on muscle hypertrophy.Previous research in the field of muscle physiology and metabolism were explored, with an emphasis on eccentric training.The supplementary experiment measured shoulder growth in response to the medial deltoid exercise called lateral raises, where different eccentric velocities were assigned to groups.Individualistic daily calorie and protein intake were controlled to ensure that sufficient nutrients were available for recovery and performance.Post-experimental research suggested that high-velocity eccentric training was best for hypertrophy due to greater levels of force production.This was consistent with the experiment, which found that those with a fast-velocity eccentric, a lower TUT, experienced greater growth.They also exhibited greater strength gain due to a neuromuscular junction adaptation.These findings are significant for designing exercise regimens that are optimal for the prevention and rehabilitation of musculoskeletal injuries and disorders.The review's findings suggest that fast-velocity eccentric contractions are ideal for increasing muscle size and strength. 3.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.005

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 head0.034
GPT teacher head0.336
Teacher spread0.302 · 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