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Record W2288852017 · doi:10.14288/1.0042295

Design of a rainfall simulator to measure erosion of reclaimed surfaces

2009· article· en· W2288852017 on OpenAlexaffabout
Les Sawatsky, Wes Dick, Dave L. Cooper, Marie Keys

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPrecipitation Measurement and Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMeasure (data warehouse)ErosionEnvironmental scienceHydrology (agriculture)SimulationComputer scienceGeologyGeotechnical engineeringGeomorphologyData mining

Abstract

fetched live from OpenAlex

Rainfall simulation is a useful tool in the analysis of soil erosion. The use of rainfall simulators has become more widespread with the development of automated instrumentation and control systems which offer a physically based system of predicting soil erosion. A variety of simulator designs have been used. This paper describes a rainfall simulator designed for analysis of erosion on steep (2.5H: 1V) reclaimed sand slopes at two oil sand mines near Fort McMurray, Alberta. The rainfall simulator applies artificial rain on a 225 m² test site divided into two side-by-side test plots. Most rainfall simulators have used a constant intensity of rainfall throughout a given simulation event. The rainfall simulator designed for this project can vary the rainfall application rate in fifteen discrete rainfall intensity increments. Therefore, it is capable of simulating a variety of non-uniform rainfall hyetographs. The rainfall simulator consists of a system of seven nozzles on each of 27 vertical support pipes. A combination of nozzles was used to simulate a desired uniform or non-uniform rainfall hyetograph with intensities ranging from 10 mm/h to 200 mm/h. The rainfall simulator also successfully simulated extreme historic and synthetic hyetographs.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.973

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 head0.022
GPT teacher head0.180
Teacher spread0.158 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2009
Admission routes2
Has abstractyes

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