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Record W2312380877 · doi:10.1115/jrc2013-2538

Modification of the Relation Between Grade and Curvature — Purpose, Reasons, and Advantages

2013· article· en· W2312380877 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCivil and Geotechnical Engineering Research
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsCurvatureRelation (database)Degree (music)MathematicsCompensation (psychology)Curve fittingMathematical analysisGeometryStatisticsComputer scienceData miningPhysics

Abstract

fetched live from OpenAlex

In cases where grades and horizontal curves are combined, the current relation between the grade and the degree of curve, D, is defined as follows:G+cD=r in which G = The maximum allowable compensated grade in %, D = Degree of curve, c = 0.04, compensation factor in % grade per degree of curve, r = The maximum grade achievable by the train in %. The above relation is a design tool to combine grade and curvature. The author intends to modify the above relation for two purposes — • to make the relation more rational for combining grade and curve for LRT design, and • to make the relation useful in computing the installation slope of special trackwork. A modified formula is suggested as under:G+cD=kr in which c = compensation factor in % grade per degree of curve determined on the basis of curvature, k = grade reduction factor arbitrarily chosen between 0.2 ∼ 1 depending on curvature and type of rail. The justification of the proposed modification and the advantages of the modified formula are discussed in details.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.134

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)0.0000.000

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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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