MétaCan
Menu
Back to cohort
Record W6931778581 · doi:10.5683/sp3/1zcufy

Experimental data of dry and saturated granular flows of increasing source volume

2022· dataset· en· W6931778581 on OpenAlex

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

VenueBorealis · 2022
Typedataset
Languageen
FieldMedicine
TopicBiomedical and Chemical Research
Canadian institutionsQueen's University
FundersLeverhulme Trust
KeywordsFlumeGranular materialFlow (mathematics)Experimental dataVolume (thermodynamics)Particle (ecology)Fluid dynamicsExperimental research

Abstract

fetched live from OpenAlex

Experimental results of granular flows in a large laboratory flume. Here we present the results of dry and initially fluid saturated or “wet” experimental flows in a large laboratory flume for five source volumes of 0.2 to 1.0 cubic metre. High-speed video was used to record particle behaviour at the grain scale. LiDAR scanning was completed on the final deposits. This dataset provides valuable test scenarios to explore the fundamental mechanisms through which the presence of a pore fluid increases flow mobility by first constraining the frictional properties of the material (dry experiments), permitting an independent evaluation of the implementation of interstitial fluid effects in numerical runout models (wet experiments).

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.060
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.329
Teacher spread0.291 · 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