Radiometric measurements of the canadian boreal forest using RADARSAT-1 beam patterns
Bibliographic record
Abstract
This paper describes exploratory work in evaluating the use of the Canadian boreal forest in potential support to radiometric calibration of the RADARSAT-1 Synthetic Aperture Radar (SAR) sensor. The primary site for SAR calibration performance and monitoring, in the Amazon rain forest, requires the use of the spacecraft's On-Board Recorder (OBR) to store the images until they can be downloaded to a Canadian data reception facility. In mid 2002, aging considerations for the OBR led to the survey of natural sites within data reception mask of Canadian ground stations. Several boreal forest and mixed Tundra-Taiga sites were tested for their ability to support radiometric analyses in case of an OBR failure. A boreal forest-type area, near Hearst, in the province of Ontario, was chosen for a more comprehensive study. Radiometric measurements covering the entire incidence angle range of RADARSAT-1 were performed using an elevation pattern measurement method adapted from the existing methodology used for Amazon image analyses. Radiometric measurements over the chosen area are reported, examining temporal and seasonal variations. Antenna gain pattern shape determination is also attempted, based on seasonal backscattering profiles of the region. The differences between extracted pattern shapes and their corresponding calibrated patterns then provide indications on the mean term, across swath backscattering behavior of the boreal forest, confirming its potential suitability for radiometric calibration monitoring.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".