Consideration of Level of Confidence within Multi-Approach Satellite-Derived Bathymetry
Bibliographic record
Abstract
The Canadian Hydrographic Service (CHS) publishes nautical charts covering all Canadian waters. Through projects with the Canadian Space Agency, CHS has been investigating remote sensing techniques to support hydrographic applications. One challenge CHS has encountered relates to quantifying its confidence in remote sensing products. This is particularly challenging with Satellite-Derived Bathymetry (SDB) where minimal in situ data may be present for validation. This paper proposes a level of confidence approach where a minimum number of SDB techniques are required to agree within a defined level to allow SDB estimates to be retained. The approach was applied to a Canadian Arctic site, incorporating four techniques: empirical, classification and photogrammetric (automatic and manual). Based on International Hydrographic Organization (IHO) guidelines, each individual approach provided results meeting the CATegory of Zones Of Confidence (CATZOC) level C requirement. By applying the level of confidence approach, where technique combinations agreed within 1 m (e.g., all agree, three agree, two agree) large portions of the extracted bathymetry could now meet the CATZOC A2/B requirement. Areas where at least three approaches agreed have an accuracy of 1.2 m and represent 81% of the total surface. The proposed technique not only increases overall accuracy but also removes some of the uncertainty associated with SDB, particularly for locations where in situ validation data is not available. This approach could provide an option for hydrographic offices to increase their confidence in SDB, potentially allowing for increased SDB use within hydrographic products.
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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 | 0.026 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".