Geophysical and Planetary Acoustics Via Balloon Borne Platforms
Bibliographic record
Abstract
Balloon-borne infrasound research began again in 2014 with a small payload launched as part of the High Altitude Student Platform (HASP; Bowman and Lees(2015)). A larger payload was deployed through the same program in 2015. These proof of concept experiments demonstrated that balloon-borne microbarometers can capture the ocean microbarom (a pervasive infrasound signal generated by ocean waves) even when nearby ground sensors are not able to resolve them (Bowman and Lees, 2017). The following year saw infrasound sensors as secondary payloads on the 2016 Ultra Long Duration Balloon flight from Wanaka, New Zealand (Bowman and Lees, 2018; Lamb et al., 2018) and the WASP 2016 balloon flight from Ft. Sumner, New Mexico (Young et al., 2018). Another payload was included on the HASP 2016 flight as well. In 2017, the Heliotrope project included a four element microbarometer network drifting at altitudes of 20-24 km on solar hot air balloons (Bowman and Albert, 2018). At the time of this writing the Trans-Atlantic Infrasound Payload (TAIP, operated by Sandia National Laboratories) and the Payload for Infrasound Measurement in the Arctic (PIMA, operated by Jet Propulsion Laboratory) are preparing to fly from Sweden to Canada aboard the PMC-Turbo balloon. The purpose of this experiment is to cross-calibrate several different infrasound sensing systems and test whether wind noise events occur in the stratosphere.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".