MODIS direct broadcast products and applications
Bibliographic record
Abstract
The MODerate resolution Imaging Spectroradiometer (MODIS) instrument provides high spatial and spectral resolution views of each point on the earth four times per day. Both Terra and Aqua platforms have a direct broadcast X-band downlink that allows MODIS (Terra) and MODIS/AIRS (Aqua) data to be received in real time by sites having the proper reception hardware. In order to facilitate use of the data, science production software is being freely distributed through the International MODIS/AIRS processing package (IMAPP). The current suite of IMAPP MODIS products includes navigation and calibration (L1B), cloud mask and cloud top properties, including thermodynamic phase, and atmospheric profiles and water vapor retrievals. The applications have been modified from the operational versions running at the Goddard Distributed Active Archive Center (DAAC) such that the only required external toolkit is NCSA HDF4. Distribution of this software provides scientists around the world with the capability to produce local real-time high spatial resolution science products. MODIS data produced from the University of Wisconsin direct broadcast automated processing is used for a variety of science applications, including calibration and product validation. The data is also being used by other institutions for a range of purposes including assisting USA National Weather Service forecasters and the monitoring of Hudson Bay shipping routes by the Canadian Ice Service. The science software is being implemented globally from Australia to South America. IMAPP has been successful in providing a portable, relatively easy to install and user friendly software package for converting direct broadcast MODIS data into valuable science products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.046 |
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".