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Record W1921065149 · doi:10.24908/pceea.v0i0.4898

THE TETON DAM FAILURE AS A SUPPORT OF AN UNDERGRADUATE COURSE OF SOIL MECHANICS

2013· article· en· W1921065149 on OpenAlexafffundvenueabout
Benoît Courcelles

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsPolytechnique Montréal
FundersPolytechnique Montréal
KeywordsHindsight biasCourse (navigation)Session (web analytics)AttractivenessDam failureIdeal (ethics)WarrantyPoint (geometry)EngineeringMathematics educationComputer scienceEngineering managementEngineering ethicsPsychologyFlood mythPolitical scienceMathematicsGeography

Abstract

fetched live from OpenAlex

To improve the attractiveness of the undergraduate course of Soil Mechanics at Polytechnique Montreal, a new approach relying on the study of the Teton Dam failure from a forensic point of view was introduced in 2012. A course evaluation performed at the end of the session demonstrated that the case study was very interesting to sensitize the students to technical and non-technical aspects, but that the formula was not ideal for active learning. Indeed, the lack of hindsight was not adequate to warranty an active participation of all students. As a consequence, a new version of the case study based on team projects is under development. The paper presents the new case study approach, the methodology and the tools under development to help the students with the case study and finally concludes with the implication of the case study in the formation of future engineers.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.005
GPT teacher head0.232
Teacher spread0.227 · 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 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

Citations1
Published2013
Admission routes4
Has abstractyes

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Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicProblem and Project Based LearningFrench-language works237,207