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Record W4389742310 · doi:10.3389/frym.2023.1239685

Keeping Your Joints Flexible Throughout Life

2023· article· en· W4389742310 on OpenAlexaff
Andreas Konrad, David G. Behm

Bibliographic record

VenueFrontiers for Young Minds · 2023
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFlexibility (engineering)DeskVariety (cybernetics)Resistance (ecology)Training (meteorology)BusinessComputer scienceEngineeringGeographyMechanical engineeringEcologyBiologyArtificial intelligenceEconomicsMeteorology

Abstract

fetched live from OpenAlex

People are built to move. To survive, not so long ago, we had to search for food every day as hunters or gatherers. In modern times, however, our way of life has changed drastically. We can buy our food at the supermarket and many people can do their work at a desk. As a result, we move less and sit for several hours every day. This is called a sedentary lifestyle. Sedentary activities can lead to a dramatic decrease in flexibility in the joints. To overcome those challenges, we can do a variety of activities such as performing sports that require the full range of motion of our joints, as well as doing stretch training, foam rolling, or resistance training.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.016

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 head0.056
GPT teacher head0.332
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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