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Record W4393882021 · doi:10.5281/zenodo.6020542

Benchmark problems for transcranial ultrasound simulation: Datasets for intercomparison of compressional wave models

2022· dataset· en· W4393882021 on OpenAlexaff
Jean‐François Aubry, Oscar Bates, Christian Boehm, Douglas A. Christensen, Carlos Cueto, Pierre Gélat, Lluís Guasch, Jiří Jaroš, Yun Jing, R.C. Jones, Ningrui Li, Patrick Marty, Hazael Montanaro, Esra Neufeld, Samuel Pichardo, Gianmarco Pinton, Aki Pulkkinen, Antonio Stanziola, Axel Thielscher, Bradley E. Treeby, Elwin van ’t Wout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldComputer Science
TopicAdvanced Computational Techniques in Science and Engineering
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBenchmark (surveying)Computer scienceTranscranial DopplerUltrasoundGeologyAcousticsGeodesyPhysicsRadiologyMedicine

Abstract

fetched live from OpenAlex

This dataset contains the skull maps and modeling results associated with the forthcoming publication "Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models".

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.032
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0060.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0210.026

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 head0.075
GPT teacher head0.302
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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