GeoComp-n, an advanced system for generating products from coarse and medium resolution optical satellite data. Part 1: System characterization
Bibliographic record
Abstract
The Geocoding and Compositing System (GeoComp) developed at the Canada Centre for Remote Sensing (CCRS), Natural Resources Canada, has been processing Advanced Very High Resolution Radiometer (AVHRR) data from the United States National Oceanic and Atmospheric Administration (NOAA) series of satellites since 1992. GeoComp (Robertson et al., 1992) was designed to produce systematic, map-compatible, multi-date composite images over large areas with reduced or no cloud content. In 1995, a revision of the original system was proposed to improve the design based on experience gained from the system's operation, to incorporate many advances in computer hardware, and to introduce value-added products resulting from the research that used GeoComp. The new system was called GeoComp-n for the "next generation" of GeoComp processors. Technical improvements designed into GeoComp-n include a modular system architecture, a fully functional operator graphical user interface and a revamped data product format. The initial version of the system was delivered in March 1999, the validation of the data layers was completed in July 1999, and the system has been used operationally at the Manitoba Centre for Remote Sensing since 2000. In this paper, the authors describe GeoComp-n and the wide range of products that are generated through its operation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".