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Record W2098314928 · doi:10.1080/02640410410001730232

Evaluation of the plumb-bob method for reading greens in putting

2004· article· en· W2098314928 on OpenAlexafffund
Sasho MacKenzie, Eric J. Sprigings

Bibliographic record

VenueJournal of Sports Sciences · 2004
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPerpendicularMathematicsBall (mathematics)GeodesyGeometryGeology

Abstract

fetched live from OpenAlex

This study evaluated the validity of the plumb-bob method as used to determine the break of a putt. Two separate experiments were conducted to examine the consequence of violating inherent assumptions in the method. In the first experiment, a controlled putting environment was constructed to assess the plumb-bob method in determining the break of a putt, if the slope of the green was not constant from the position of the golfer behind the ball through to the hole. It was determined that if the slope of the green beneath the golfer was different from the slope between the ball and the hole, then the plumb-bob method would provide an incorrect indication of break. The second experiment examined the ability of a golfer to stand perpendicular to a slope. Half of the participants in the study deviated by +/-1.5 degrees or greater from standing perpendicular to a slope. A + 1.5 degrees error on a 1.4 m (approximately 4.5 ft) putt translates into reading an extra 0.08 m of break and a missed putt. The plumb-bob method was found to be an invalid system for determining the break of a putt.

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)0.016
metaresearch head score (Gemma)0.124
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 score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.124
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.308
Teacher spread0.278 · 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

Citations9
Published2004
Admission routes2
Has abstractyes

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