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Record W2060253565 · doi:10.1115/fedsm2002-31429

Calculation of Debris Trajectories During High-Speed Snowplowing

2002· article· en· W2060253565 on OpenAlexaff
Hany Nakhla, Brian E. Thompson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsWestern University
Fundersnot available
KeywordsVisibilitySplashSnowDebrisTurbulenceMeteorologyTrajectoryMarine engineeringTrack (disk drive)Environmental scienceSimulationAerospace engineeringMechanicsComputer sciencePhysicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

An engineering model is presented to calculate the trajectory of airborne debris that adversely affects visibility during high-speed snow plowing. Reynolds-averaged Navier-Stokes equations are solved numerically with turbulence-modeling, particle-tracking, and cutting-edge approximations. Results suggest snow can be divided into splash and snow-cloud when designing treatments to improve visibility for snowplow drivers and following traffic. Calculated results confirm the findings of windtunnel and road tests, specifically that the trap angle of overplow deflectors should be less than 50 degrees to eliminate snow debris blowing over top of the plow onto the windscreen.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.241
Teacher spread0.225 · 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.

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

Citations1
Published2002
Admission routes1
Has abstractyes

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