Comparison of Satellite and Ground-Based Data on Polar Mesospheric Clouds
Bibliographic record
Abstract
In preparation for coordinated ground-based optical measurements with the recently launched NASA Aeronomy of Ice in the Mesopause (AIM) satellite we have examined data mainly from the Solar Backscatter Ultraviolet (SBUV) instruments onboard the NOAA polar orbiting satellites. Our primary goal is to investigate the detection of Polar Mesospheric Clouds (PMCs) over the North American continent using data over five consecutive years (2001-2005). PMCs are ice clouds that form near the extremely cold (<150K) mesopause region (80-85 km) during the summer months at high-latitudes. From the ground, these clous can be seen during twilight hours as Noctilucent or "night shining" Clouds (NLC). In particular, SBUV satellite observations have shown that the occurrence and brightness of PMCs have been growing over the last several decades prompting speculation concerning their role in climate change. In this poster we compare reports of displays seen from the ground over the North American continent primarily by observers participating in the Canadian Noctilucent Cloud Observing Network CAN AM with the SBUV as well as new Ozone Monitoring Instrument (OMI) satellite data. Our focus is to investigate the occurrence and spatial extent of the clouds, as well as to identify unusual low latitude events (<50 deg) that have occasionally been seen as far south as Logan, Utah.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".