Tele-infrasonic studies of hard-rock mining explosions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".