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Record W4385479349 · doi:10.1139/cgj-2022-0464

Limitations of Gold’s formula for predicting ice thickness requirements for heavy equipment

2023· article· en· W4385479349 on OpenAlexaffvenue
Alan Fitzgerald, Willem Janse van Rensburg

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsFlexural strengthContext (archaeology)Ultimate tensile strengthMetric (unit)Geotechnical engineeringCivil engineeringStructural engineeringEngineeringEnvironmental scienceForensic engineeringComputer scienceGeologyMaterials scienceComposite materialOperations management

Abstract

fetched live from OpenAlex

Common practice for determining the required ice thickness for vehicles and equipment relies on Gold’s formula as outlined in provincial and territorial publications relating to ice safety. This practice persists despite recent advances in ice engineering knowledge that provide more comprehensive design methods utilizing allowable stress design approaches. The authors have identified that the use of Gold’s formula for determining required ice thickness may lead to unsafe practices when utilized in the context of heavy construction equipment, increasing the risk of ice breakthrough to personnel and equipment. The authors use recent design examples to demonstrate instances in which the use of Gold’s formula results in predicted flexural tensile stresses in the ice cover that exceed the maximum design stress recommended in contemporary literature. In the case of large excavators (53 metric tonnes) and heavy dozers (40 metric tonnes), as examples, the use of Gold’s formula for determining the required ice thickness will result in predicted flexural tensile stresses that exceed the recommended maximum design stress by 56%–71%.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.066
GPT teacher head0.271
Teacher spread0.205 · 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 designNot applicable
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

Citations4
Published2023
Admission routes2
Has abstractyes

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