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Record W4394518710 · doi:10.6084/m9.figshare.19097118

Experimental study of bed morphology evolution under two-dimensional dam-break flow

2022· dataset· en· W4394518710 on OpenAlexaff
Ying Liu, Chen Yang, Xin Chen

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsUniversity of British ColumbiaBC Cancer AgencyUniversity of Toronto
Fundersnot available
KeywordsMorphology (biology)Dam breakFlow (mathematics)GeologyMechanicsMaterials scienceGeographyPhysicsPaleontologyArchaeology

Abstract

fetched live from OpenAlex

The present study discusses the evolution of bed morphology under 2D dam-break flow by conducting a set of laboratory experiments. The experiments were conducted in a 1.6 m × 28 m × 1.0 m glass flume with non-uniform fly ash as the bed material. Two scenarios were designed: no inflow (scenario 1) and nearly constant water level (scenario 2) in the upstream reservoir. During the experiment, the water levels were measured by pressure sensors which were buried under the sediment layer, and the bed morphology was measured by an ultrasonic ranging system. The results showed that: (1) in scenario 1, the range and depth of the scour pit continued to increase, but the location of the deepest scour point did not change significantly; (2) in scenario 2, the depth and range of scour increased after the dam break, but the growth rate slowed down obviously after t = 15 min. The data can be employed to understand the bed morphology evolution under a dam-break flow, validate numerical approaches, and test geomorphic flood models.

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)0.001
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.043
GPT teacher head0.267
Teacher spread0.224 · 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
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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