Guide de production d'imagerie sonar à l'aide d'outils grand public - Étude de cas à la rivière des Outaouais à Quyon, Québec
Bibliographic record
Abstract
Natural Resources Canada (NRCAN) has the mandate of providing essential geographic information. An improved knowledge of our physical environment represents one of the basis of this mandate. This essential baseline information is generally associated with the terrestrial or landmass environment. The geographic knowledge of the underwater environment often represents the weak segment in the chain linking terrestrial units together. In the geohazard domain, also a NRCAN mandate, the development of mitigation strategies for geohazards, including landslide, rely on accurate underwater terrain information. Recently, there have been consumer grade, low cost, side scan imaging sonars capable of imaging the floor of a lake, a river or a coastline. These tools are targeted for sport fishing and diving markets but they are also capable of partly providing an image view of the lake and river floor. These tools, which are versatile, portable and easy to use can have scientific and technological applications, such as the subsurface geomorphological mapping of lake and river floors: bedrock outline, contact between sediments and bedrock, lineaments, sand waves location and various forms of erosion / deposit. Therefore, low cost consumer grade side scan sonars can partly fill the technology / scientific information gap for essential geographic information and geohazards, and are particularly well suited for the Canadian North and in remote or difficult to access areas. A case study was carried out in Quyon Qc. area, in the Ottawa region, to determine the potential of these tools for the geomorphological mapping of a river floor, as it may relate to landslide characterization in the area. This document explains the main constraints for a high quality survey and recommends ways to overcome them. Several image examples and interpretation are presented as well as the findings resulting from this case study.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.122 | 0.064 |
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".