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Record W1880127215 · doi:10.1002/esp.3361

Gravel‐bed rivers: from particles to patterns

2012· article· en· W1880127215 on OpenAlexaff
Peter Ashmore, Colin D. Rennie

Bibliographic record

VenueEarth Surface Processes and Landforms · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of OttawaWestern University
Fundersnot available
KeywordsTemporal scalesRange (aeronautics)Scale (ratio)Field (mathematics)GeologyFocus (optics)Hydrology (agriculture)River morphologySpatial ecologyEnvironmental resource managementEnvironmental scienceGeomorphologyGeotechnical engineeringGeographyCartographyEcologySedimentEngineering

Abstract

fetched live from OpenAlex

ABSTRACT The papers in this special issue reflect several of the major themes and topics from the 7 th International Workshop on Gravel‐Bed Rivers. The papers focus primarily on aspects of bed material transport in gravel‐bed rivers and larger scale morpho‐dynamics. Research in gravel‐bed rivers is increasingly integrating processes over a wide range of temporal and spatial scales by combining field observation, lab experimentation, numerical modeling and theory testing in a range of river types, aided by new technological developments in particle tracking, computational modeling and high resolution spatial data. This is leading to greater understanding of the processes leading to distinctive morpho‐dynamics of river types and a more reliable basis for river management. Copyright © 2012 John Wiley & Sons, Ltd.

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 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 score0.064
Threshold uncertainty score0.998

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)0.0030.001

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 head0.011
GPT teacher head0.211
Teacher spread0.201 · 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.

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

Citations11
Published2012
Admission routes1
Has abstractyes

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