Effect of interarray elevation differences on infrasound beamforming
Bibliographic record
Abstract
The International Monitoring System infrasound network will, upon completion, contain 60 microbarometer arrays with apertures of between 1 and 4 km. These arrays are located within a variety of terrains, leading to large ratios of interelement elevation differences to array aperture for those arrays situated in areas of significant topography. Systematic errors in beamforming estimates caused by neglecting the vertical extent of the arrays, are quantified for both signal backazimuth and apparent velocity. Of the 43 arrays certified as of 2011 January, I14CL on Juan Fernandez Island has the greatest topography across an array, with a least-squares fitted plane through the array elements having an 8.1° slope from the horizontal (compared to a network mean of 1.6°). Beamforming errors (both backazimuth and apparent velocity) are a function of the arrival azimuth and become increasingly large for steeply inclined arrivals, such that systematic errors will be significantly larger for signals returned from the thermosphere compared to those from the stratosphere. At several arrays, azimuthal errors due to array topography are comparable in magnitude to deviations often associated with atmospheric propagation. These findings are illustrated using signals recorded in Greenland at I18DK, where differences between results processed using both full 3-D array geometry and the 2-D (topography neglected) approximation exhibit good correspondence to theoretical predictions.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".