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Record W2108123151 · doi:10.1139/t10-010

A review of 41 legal cases involving geotechnical practice in Canada

2010· review· en· W2108123151 on OpenAlexafffundvenueabout
Suhail Abdulahad, George Jergeas, Janaka Y. Ruwanpura

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typereview
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsBiddingGeotechnical investigationFoundation (evidence)EngineeringGeotechnical engineeringCivil engineeringForensic engineeringBusinessPolitical scienceLaw

Abstract

fetched live from OpenAlex

This paper explores factors that contribute to claims of changed soil conditions in infrastructure and building projects through a limited review of Canadian legal cases. Forty-one cases, relating to geotechnical problems covering a period of 25 years (1982–2006), were studied. The information obtained was categorized according to the causes for geotechnical claims. The most frequent causes of geotechnical disputes were found to be different soil conditions and recommendations than expected from those given in the geotechnical report, inaccuracies in the design plans and specifications, and the owner’s failure to disclose important information. The paper also discusses the special requirements for preparing geotechnical information for construction and suggests precautionary measures to reduce the number of disputes or to arrive at an equitable resolution. These measures require more proactive planning and may require additional funds before the bidding stage to perform a more detailed soil investigation, keep communication clear among all participants, and make a greater effort to provide accurate designs and specifications.

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)0.005
metaresearch head score (Gemma)0.017
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.192
Threshold uncertainty score0.385

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0220.036
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.253
Teacher spread0.238 · 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
GenreReview

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
Published2010
Admission routes4
Has abstractyes

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