The polar mesospheric cloud dataset of the Balloon Lidar Experiment (BOLIDE)
Bibliographic record
Abstract
Abstract. The Balloon Lidar Experiment (BOLIDE) observed polar mesospheric clouds (PMCs) along the Arctic circle between Sweden and Canada during the balloon flight of PMC Turbo in July 2018. The purpose of the mission was to study small-scale dynamical processes induced by the breaking of atmospheric gravity waves by high-resolution imaging and profiling of the PMC layer. The primary parameter of the lidar soundings is the time- and range-resolved volume backscatter coefficient β. These data are available at high resolutions of 20 m and 10 s (Kaifler, 2021, https://doi.org/10.5281/zenodo.5722385). This document describes how we calculate β from the BOLIDE photon count data and balloon floating altitude. We compile information relevant for the scientific exploration of this dataset, including statistics, mean values, and temporal evolution of parameters like PMC brightness, altitude, and occurrence rate. Special emphasis is given to the stability of the gondola pointing and the effect of resolution on the signal-to-noise ratio and thus the detection threshold of PMC. PMC layers were detected during 49.7 h in total, accounting for 36.8 % of the 5.7 d flight duration and a total of 178 924 PMC profiles at 10 s resolution. Up to the present, published results from subsets of this dataset include the evolution of small-scale vortex rings, distinct Kelvin–Helmholtz instabilities, and mesospheric bores. The lidar soundings reveal a wide range of responses of the PMC layer to larger-scale gravity waves and breaking gravity waves, including the accompanying instabilities, that await scientific analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".