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Record W2464517689 · doi:10.5539/ass.v12n8p154

A Research on Speed-Centered Pre-competition Training of 1500 Meters Sportsman

2016· article· en· W2464517689 on OpenAlexvenueno aff
Mingxia Wang

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsIntensionTrack and field athleticsTraining (meteorology)Race (biology)Competition (biology)Track (disk drive)Event (particle physics)IdeologySpeed skatingComputer scienceDistance runningPsychologySociologyAthletesSimulationPolitical sciencePhysical medicine and rehabilitationGender studiesGeographyPhysical therapyMeteorologyMedicine

Abstract

fetched live from OpenAlex

This paper starts from the characteristic of 1500 meters sports to explore the method of speed capacity training. Middle-distance race is a speed endurance event in track and field sports, the character of which is its high intension muscle movement for a long time. The more competitive and the nearer level of the sports, the higher request of the speed is asked. This article analyses and discusses the development of modern middle-distance race characteristics by literature data, statistics and antitheses. It points out that the guiding ideology "speed training as the center" and high speed ability training is the main and effective ways of training middle-distance race nowadays.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.082
GPT teacher head0.394
Teacher spread0.311 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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