Bibliographic record
Abstract
A typical infrasound array element consists of an infrasonic pressure sensor connected to a multiple-pipe or porous-hose system for reduction of wind noise. While the frequency response of the sensor itself may be known, that response is modified by the wind-noise reduction system. One approach to measuring the frequency response of the complete system is to perform a comparison calibration using ambient noise and a co-located reference sensor with sufficiently low self-noise and well characterized frequency response. In the technique discussed here, the reference sensor is a virtual reference constructed by summing the outputs of two or three calibrated microphones; symmetric placement of the microphones places the phase center of the virtual reference at the geometric center of the pipe system. Proper combination of auto- and cross-spectral averages over a several-hour period produces an estimate of the response of the infrasound system relative to that of the virtual reference. Measured coherence and the consistency between the magnitude and the phase of the response provide quality checks on the process. This approach has been demonstrated at infrasound monitoring sites in Washington, Alaska, Manitoba, Austria, and Antarctica. [Funded by the US Army Space and Missile Defense Command.]
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".