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Record W3043600528 · doi:10.1115/hvis2019-113

Investigating Pressure Wave Impact on a Surrogate Head Model Using Numerical Simulation Techniques

2019· article· en· W3043600528 on OpenAlexaff
Rohan Banton, Thuvan Piehler, Nicole E. Zander, Richard Benjamin, Josh Duckworth, Oren E. Petel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsBlast waveExplosive materialDetonationHead (geology)Pressure waveComputer simulationSurrogate modelTraumatic brain injuryMechanism (biology)EngineeringComputer scienceAeronauticsAerospace engineeringSimulationShock waveMedicinePhysicsMechanicsGeologyHistory

Abstract

fetched live from OpenAlex

Abstract There is an urgent need to understand the mechanism leading to mild traumatic brain injury (mTBI) resulting from blast wave impact to the head. The recent conflicts in Iraq and Afghanistan have heightened the awareness of head impact injuries to military personnel resulting from exposure to blast waves [1, 2]. A blast wave generated in air is a by-product of the detonation of an explosive [3]. To date the mechanism resulting in mTBI from primary blast insult is still unclear.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.508

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)0.0000.000

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 head0.173
GPT teacher head0.492
Teacher spread0.319 · 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 designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

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