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Record W2095811170 · doi:10.1080/02640410802509136

Peaking for optimal performance: Research limitations and future directions

2009· editorial· en· W2095811170 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueJournal of Sports Sciences · 2009
Typeeditorial
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsTaperingLeagueAthletesTime trialCompetition (biology)Computer scienceDuration (music)Operations researchPhysical therapyPhysical medicine and rehabilitationMedicinePsychologySimulationAeronauticsApplied psychologyEngineering

Abstract

fetched live from OpenAlex

A key element of the physical preparation of athletes is the taper period in the weeks immediately preceding competition. Existing research has defined the taper, identified various forms used in contemporary sport, and examined the prescription of training volume, load, intensity, duration, and type (progressive or step). Current limitations include: the lack of studies on team, combative, racquet, and precision (target) sports; the relatively small number of randomized controlled trials; the narrow focus on a single competition (single peak) compared with multiple peaking for weekly, multi-day or multiple events; and limited understanding of the physiological, neuromuscular, and biomechanical basis of the taper. Future research should address these limitations, together with the influence of prior training on optimal tapering strategies, and the interactions between the taper and long-haul travel, heat, and altitude. Practitioners seek information on how to prescribe tapers from season to season during an athlete's career, or a team's progression through a domestic league season, or multi-year Olympic or World Cup cycle. Practical guidelines for planning effective tapers for the Vancouver 2010 and London 2012 Olympics will evolve from both experimental investigations and modelling of successful tapers currently employed in a wide range of sports.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.315
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.088
GPT teacher head0.400
Teacher spread0.312 · 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