MétaCan
Menu
Back to cohort

Use of Index Gradients and Default Tailwater Depth as Aids to Hydraulic Modeling of Flow-Through Rockfill Dams

2012· article· en· W2156060372 on OpenAlex
David Hansen, Ali Roshanfekr

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

VenueJournal of Hydraulic Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTailwaterHydraulic structureFlow (mathematics)Hydraulic headGeologyParametric statisticsGeotechnical engineeringHydraulicsEngineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

To assess the potential for unraveling failure of flow-through rockfill dams, a systematic study of three aspects of the hydraulic design of these structures was conducted. First, the gradient that is most useful in independently computing the height of the point of first flow emergence was established. The proposed method is based on the idea of the angle of the emergent flow field within the toe of the structure. Secondly, as a result, this study presents a method for independently computing the variation in hydraulic head within the vertical that allows the toe of the structure (i.e., downstream from the vertical associated with first flow emergence) to be isolated. This is based in part on a separate parametric study of 24 numerically simulated flow-through rockfill dams. Thirdly, the gradient that allows for the independent estimation of the default tailwater depth is presented and verified, with the help of laboratory results. The hope is that these three computational tools will facilitate the design and assessment of flow-through rockfill structures, as a particular class of pseudohydraulic structure.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.230
Teacher spread0.211 · 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