Bibliographic record
Abstract
Numerous signals will be routinely detected using the 60 array, global IMS (International Monitoring System) infrasound network. Infrasonic signals are sub-audible quasi longitudinal, atmospheric waves in the frequency band from about 10 Hz to -5 minutes in period (limited by human acoustic audibility in the high frequency limit and by the wave-guide acoustic cut-off frequency and the Brunt Vaisalla frequency in the low frequency limit) These small amplitude waves are a natural subset of the well-known atmospheric acoustic-gravity wave regime which has been identified from the linearized equations of geophysical fluid mechanics in the flat earth approximation, neglecting the earth's rotation, etc. For the IMS network the instrumental pressure sensor response was chosen to range from -4 to 0.02 Hz. These are ground-based arrays of typically 4 to 9 sensors with separations of about 1-2 km between the array elements. Examples of naturally occurring impulsive sources of infrasound include volcanic eruptions, earthquakes, bolides (large meteor-fireballs entering the atmospheric at very high speeds up to -300 times faster than ground-level sound waves), microbaroms (the 'voice of the sea' due to the interaction of atmospheric storms and surface ocean waves) and the supersonic motion of the auroral electrojet at about 100 km altitude (auroral infrasonic waves), etc. In this paper we will briefly summarize our current state of knowledge of infrasound signals from bolides. This summary will include the generation of the signals at the complex, quasi-cylindrical line source, to the refraction and diffraction of the propagating waves by the middle atmospheric and tropospheric temperature and wind systems and finally, the detection of the signals and their interpretation by inferring the source properties, Le., source altitude, blast radius (see below) and the source energy, etc. In addition, we will use infrasound from energetic bolides to estimate the expected steady state, global influx rate of these large bodies as a hnction of the bolide source energy. Further details on this subject can also be found in recent publications by the author.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".