Integration of a Helicopter-Based Ground Penetrating Radar (GPR)with a Laser, Video And GPS System
Bibliographic record
Abstract
Helicopter-borne sensors have been used since the early 1990s to monitor ice properties in support of winter marine transportation along the East Coast of Canada. The observations are used directly in ice chart production and indirectly in the validation of ice hazard identification algorithms using satellite imagery, the main source of data for ice chart production. Called the VideoGPS Sensor System, this system includes a laser altimeter, digital image capture and a GPS receiver for positioning. This system is usually operated concurrently with an electromagnetic-based ice thickness sensor. A commercial off the-shelf ground penetrating radar (GPR) system has recently been integrated into the VideoGPS system. The VideoGPS system has a laser altimeter to measure flying height and ice roughness. Digital images are collected with enough overlap so they can be mosaicked to provide a two-dimensional image of the ice surface along the helicopter’s flight path and complement the detailed one-dimensional laser roughness profile. A Matlab-based data processing environment with a graphical user interface has been developed to handle the data integration and the display of the results. The ground penetrating radar (GPR) system has been added to the VideoGPS system for snow thickness measurements. As snow and ice thickness measurements using airborne GPR have been used for over 30 years, the evaluation of GPR performance was to ensure the overall system packaging performed properly, not to evaluate whether or not GPR can measure snow and ice thickness. References going back thirty years are provided. Due to limited snow cover during the field trials of 2007 and 2008 in Prince Edward Island, Canada, the integrated system has been evaluated with data collected over a frozen fresh water lake. GPR data results are shown which include the laser altimeter flying height for evaluation purposes. GPR data were processed for freshwater ice thickness for several parallel flight lines and these are shown as a colour-coded result on a digital image mosaic of the flight path. In addition, recent snow thickness measurement results from April 2008 in the eastern Canadian Beaufort Sea are presented.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.002 |
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".