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Record W4223475377 · doi:10.1680/jgeen.21.00008a

Performance of geotextile roofing felts and natural grass roots in a cricket pitch

2022· article· en· W4223475377 on OpenAlexaff
Md. Mostafiz, Md. Zoynul Abedin, Naveel Islam, Md Kausar, Rajib Dey, Arun J. Valsangkar

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Geotechnical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsGeotextilePenetrometerCricketGeotechnical engineeringMaterials scienceMoistureComposite materialEnvironmental scienceGeologySoil waterSoil scienceEcology

Abstract

fetched live from OpenAlex

The bounce and penetrometer resistance (PR) of a cricket pitch for three different crack-control conditions was investigated. In total, 15 miniature cricket pitches were prepared using the conventional procedure, natural grass roots and geotextile roofing felts combined with varying proportions of fine sand and bentonite clay. The rebound ball height and ground PRs were measured with a designed bounce meter and pocket penetrometer, respectively. The properties of the pitch soils (e.g. crack width, moisture content and field densities) were also measured. The results of the test programme indicated that the pitch with the geotextile crack-control system provided a higher coefficient of restitution (CoR) and PR than the other two systems. The test results also suggested that, in order to reduce significant growth of crack widths to provide a good cricket pitch, the clay content under geotextile roofing felt should be kept at 50–65%. Several equations relating the CoR and PR with crack width and the physical properties of the pitch soil were developed for use in pitch characterisation.

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 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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.003
GPT teacher head0.164
Teacher spread0.161 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Geotechnical EngineeringSame topicSports Dynamics and BiomechanicsFrench-language works237,207