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Record W2026994748 · doi:10.1121/1.2741375

Tele-infrasonic studies of hard-rock mining explosions

2007· article· en· W2026994748 on OpenAlexaff
Mihan H. McKenna, Brian W. Stump, Sylvia Hayek, Jason R. McKenna, Terry R. Stanton

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsGeologyInfrasoundSeismologyEnvironmental scienceAcousticsPhysics

Abstract

fetched live from OpenAlex

The Lac-du-Bonnet infrasound station, IS-10, and the Minnesota iron mines 390 km to the southeast are ideally located to assess the accuracy of atmospheric profiles needed for infrasound modeling. Infrasonic data from 2003 associated with explosions at the iron mine were analyzed for effects of explosion size and atmospheric conditions on observations with well-constrained ground truth. Noise was the determining factor for observation; high noise conditions sometimes prevented unequivocal identification of infrasound arrivals. Observed arrivals had frequencies of 0.5 to 5 Hz, with a dominant frequency of 2 Hz, and generally had durations on the order of 10 s or less. There was no correlation between explosive amount and observability. Tele-infrasonic propagation distances (greater than 250 km) produce thermospheric ray paths. Modeling is based upon MSIS/HWM (Mass Spectrometer Incoherent Scatter/Horizontal Wind Model) and NRL-G2S (Naval Research Laboratory Ground to Space) datasets. The NRL-G2S dataset provided more accurate travel time predictions that the MSIS/HWM dataset. PE modeling for the NRL-G2S dataset indicates energy loss at higher frequencies (around 4 Hz). Additionally, applying the Sutherland/Bass model through the NRL-G2S realization of the atmosphere in InfraMAP results in predicted amplitudes too small to be observed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.228

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 studies0.0000.000
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 head0.032
GPT teacher head0.272
Teacher spread0.240 · 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

Citations26
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicSeismic Waves and AnalysisFrench-language works237,207