TVC Experiment 2018/19: Radarsat-2 backscatter data
Bibliographic record
Abstract
This dataset contains the processed, backscatter data from Radarsat-2 (RSAT-2) satellite data, as part of Environment and Climate Change Canada's 2018-2019 Trail Valley Creek Snow Experiment (TVC Experiment 18/19). These RSAT-2 data were collected and processed evaluate against a network of Steven’s HydraProbe soil monitoring sensors, and coincident in situ snowpit measurements, airborne radar, and other satellite radar data to better understand soil-snow-radar interactions in a tundra environment. The RSAT-2 data was ordered to provide wintertime coverage from September 2018 to July 2019, over the Trail Valley Creek research station (https://www.trailvalleycreek.ca/) in Northwest Territories, Canada. Three periods of in situ snow measurement took place in November 2018, January 2019, and March 2019. RSAT-2 data was acquired in Wide Fine Quad mode HH+HV+VH+VV. The RSAT-2 products were processed using the European Space Agency’s (ESA), Sentinel Application Platform (SNAP) software which included image calibration to sigma nought and orthorectification. An average of the calibrated backscatter and incidence angles was then calculated for an area 100 x 100 meters surrounding the geographic coordinates of each snowpit.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.021 |
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".