STAR MODIS Cloud Satellite Dataset. Version 1.0
Bibliographic record
Abstract
The changing climate represents one of the most important ecological issues concerning our planet. Changes in atmospheric trace gases, cloud over, cloud type, solar radiation, and tropospheric aerosols (liquid or solid particles suspended in the air) can have a profound impact on the climatic system as each property can modify the climate through changes in the radiative forcings. As such, it is important to correctly represent the climate and the associated forcings in conceptual and predictive global models. In an effort to monitor these properties, two MODIS (Moderate Resolution Imaging Spectroradiometer) instruments, the first launched on 18 December 1999 onboard the Terra Satellite Platform and the second on 4 May 2002 onboard the Aqua Satellite platform, were uniquely designed to observe and monitor these and other Earth changes on a global scale. A thorough description of global cloudiness and its associated properties is essential to the MODIS mission for two reasons. First, clouds play a critical role in the radiative balance of the Earth and must be accurately described in order to assess climate and potential climate change accurately. In addition, the presence or absence of cloudiness must be accurately determined in order to retrieve properly many atmospheric and surface parameters, including phase, optical thickness, and temperature. These and other cloud parameters are retrieved at a high resolution, allowing many details and fine features to be resolved accurately. The MODIS Cloud Product combines infrared and visible techniques to determine both physical and radiative cloud properties. Daily global Level 2 data are provided. Cloud-particle phase (ice vs. water, clouds vs. snow), effective cloud-particle radius, and cloud optical thickness are derived using the MODIS visible and near-infrared channel radiances. Cloud-top temperature, height, effective emissivity, phase (ice vs. water, opaque vs. non-opaque), and cloud fraction are produced by the infrared retrieval methods both day and night at 5 × 5 1-km-pixel resolution. Finally, the MODIS Cloud Product includes the cirrus reflectance in the visible at the 1-km-pixel resolution, which is useful for removing cirrus scattering effects from the land-surface reflectance product. There are two MODIS Cloud data product files: MOD06_L2, containing data collected from the Terra platform; and MYD06_L2, containing data collected from the Aqua platform. For further details see the NASA MODIS home page: http://modis-atmos.gsfc.nasa.gov/MOD06_L2/index.html)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.029 |
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".