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Record W2040346055 · doi:10.2113/jeeg10.1.1

Golf Course Applications of Near-Surface Geophysical Methods: A Case Study

2005· article· en· W2040346055 on OpenAlexaffabout
Barry J. Allred, J. D. Redman, Edward L. McCoy, Richard S. Taylor

Bibliographic record

VenueJournal of Environmental and Engineering Geophysics · 2005
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsDuPont (Canada)Research Canada
Fundersnot available
KeywordsGround-penetrating radarCourse (navigation)EMIGeologyRadarGeophysicsRemote sensingEngineeringTelecommunicationsElectromagnetic interference

Abstract

fetched live from OpenAlex

Abstract As of the year 2000, there were over 15,000 golf course facilities in the U.S.A. alone. The upkeep of these facilities requires continual maintenance and occasional remodeling. The superintendents and architects responsible for the maintenance and remodeling efforts need non-destructive tools for obtaining information on shallow subsurface features within parts of the golf course, particularly tees and greens. The subsurface features of importance include, but are not limited to, constructed soil layer characteristics and drainage system infrastructure. Near-surface geophysical methods can potentially provide a non-destructive means for golf course superintendents and architects to obtain the shallow subsurface information required to address their maintenance and remodeling concerns. This case study assessment of near-surface geophysical methods in regard to golf course applications focused on electromagnetic induction (EMI) and ground penetrating radar (GPR) techniques. The investigation employed two different EMI ground conductivity meters. Two GPR systems were also tested including the evaluation of antenna center frequencies ranging from 250 to 1,000 MHz. The study incorporated three separate sites. Measurements with both EMI and GPR were collected on a tee and a green at the Muirfield Village Golf Club in Dublin, Ohio, U.S.A. and on a practice green at the Golf Club of Dublin in Dublin, Ohio, U.S.A. GPR was also tested on a golf course green at the Guelph Turfgrass Institute & Environmental Research Centre in Guelph, Ontario, Canada. Results indicate that use of the appropriate EMI equipment can provide information on spatial changes of shallow apparent electrical conductivity (ECa) within golf course green constructed soil layers. This ECa data could potentially be employed to gauge constructed soil layer conditions, including wetness, salinity, etc., within different areas of a green. GPR proved quite capable of obtaining useful information on the golf course tee and greens that were studied, at least in regard to constructed soil layer thicknesses/depths or their areal extent and in locating the subsurface drainage pipe systems present. For the GPR center antenna frequencies evaluated, ranging from 250 to 1,000 MHz, all seemed to work relatively well for mapping tee and green constructed soil layer areal extent and drainage pipe locations. The higher frequency 900 and 1,000 MHz antennas appeared to work best for resolving thicknesses/depths of constructed soil layers within the tee and greens investigated. In addition, computer modeling of synthetic GPR profiles provide valuable insight and help considerably with data interpretation. While more research is certainly warranted, near-surface geophysical methods, especially EMI and GPR, appear to show promise with respect to acquiring the data needed in golf course maintenance and remodeling applications.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.253
Teacher spread0.246 · 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

Citations16
Published2005
Admission routes2
Has abstractyes

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