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Record W4385981519 · doi:10.1051/e3sconf/202341502003

The Rosetta Stone Project – Integrating experimental results on debris flow mechanics across the scales: Next results

2023· article· en· W4385981519 on OpenAlexaff
Elisabeth T. Bowman, Amanda Fawley, Roland Kaitna, Shuai Li, Brian W. McArdell, Jim McElwaine, Nicoletta Sanvitale, W. Andy Take, Lisa Tauskela, Alex Taylor-Noonan, Yuting Zhao

Bibliographic record

VenueE3S Web of Conferences · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsQueen's University
Fundersnot available
KeywordsDebris flowDebrisKey (lock)Computer scienceFlow (mathematics)Scale (ratio)GeologyMechanicsPhysics

Abstract

fetched live from OpenAlex

We present the combined efforts of a research network designed to address the many challenges in the experimental modelling of debris flow phenomena. The approach has been to use apparatuses of different functional arrangement and at different scale with identical and commonly sourced flow materials from the highly idealised (dry, coarse and uniform) to the highly complex (well graded, segregating, fluid saturated). Here we briefly present some key findings of the network and point to the research questions that are currently being addressed. This complementary view of experimental debris flows helps to constrain methodological artefacts/scale effects and to identify key processes responsible for the diverse appearance and often high mobility of debris flows.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.296
Teacher spread0.266 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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