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Record W2889852317 · doi:10.1051/e3sconf/20184006025

Shore-based monitoring of flow dynamics in a steep bedrock canyon river

2018· article· en· W2889852317 on OpenAlex
Saber Ansari, Colin D. Rennie, Jeremy G. Venditti, Eva Kwoll, Kirsti Fairweather

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

VenueE3S Web of Conferences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of VictoriaSimon Fraser UniversityUniversity of Ottawa
Fundersnot available
KeywordsCanyonBedrockGeologyShoreGeomorphologyHydrology (agriculture)Flow (mathematics)OceanographyGeotechnical engineering

Abstract

fetched live from OpenAlex

The pace of landscape evolution is set by bedrock erosion in canyons. This phenomenon occurs by various geological processes including plucking of bedrock blocks and abrasion by saltating bedload and suspended load in highly turbulent flows. For a better understanding of the river flow characteristics in bedrock rivers, a comprehensive study of flow dynamics was undertaken in Black Canyon in the Fraser River, British Columbia. We used shore-based video imagery of the river to study surface flow dynamics. The shore-based monitoring system consisted of a Campbell Scientific camera mounted at the top of the canyon walls. We monitored the water surface boils due to upwelling and determined river surface flow velocities from the shore-based imagery. Automatic detection of the upwelling surface boils leads to a better understanding of the secondary circulation patterns and flow structures in this large steep river bedrock canyon. The data collection and analytical procedures developed in this research are cost-effective tools for remotely determining flow dynamics, which can be applied to other rivers.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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