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Record W2746344244 · doi:10.3390/geosciences7030074

Experimental Investigation of Debris-Induced Loading in Tsunami-Like Flood Events

2017· article· en· W2746344244 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGeosciences · 2017
Typearticle
Languageen
FieldEngineering
TopicEarthquake and Tsunami Effects
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaWaseda UniversityMinistry of Education, Culture, Sports, Science and Technology
KeywordsDebrisDebris flowFlood mythGeotechnical engineeringGeologyDragEnvironmental scienceEngineeringGeographyOceanographyAerospace engineering

Abstract

fetched live from OpenAlex

Debris loads during flood events have been well-documented by forensic engineering field surveys of affected communities. Research has primarily focused on debris impact loading and less emphasis has been placed into quantifying the loads and effects associated with debris damming, which occurs when solid objects accumulate at the front of structures. The formation of the debris dam has been shown to results in increased drag forces, backwater rise, and flow accelerations which can influence the stability of the structure. This study examined the formation of a debris dam in steady-state conditions of debris common to flood-prone communities. The study determined that the hydraulic conditions, in particular flow velocity, influenced the formation of the debris dam. Additionally, the study examined the influence of the blockage ratio on the backwater rise as well as the drag coefficient.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.311

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 head0.021
GPT teacher head0.255
Teacher spread0.234 · 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