USING AIRBORNE LIDAR TO MAP EXPOSURE OF COASTAL AREAS IN MARITIME CANADA TO FLOODING FROM STORM-SURGE EVENTS: A REVIEW OF RECENT EXPERIENCE
Bibliographic record
Abstract
Much of the coast in the Canadian Maritimes is susceptible to erosion and flooding from storm-surge events and long-term sea-level rise. In recent years significant damage has occurred during storms in both urban and rural areas of the region. LiDAR technology has been employed to develop high-resolution digital elevation models (DEMs) as a basis for production of flood-risk maps. These are required by coastal zone managers and emergency measures officials to plan for the future. This paper presents a summary of the LiDAR surveys conducted in the region to date and reviews the conclusions on exposure to flooding in each area. Although most of the areas considered lie above mean sea level, some areas are dyked and lie below mean sea level. These areas are assessed in terms of their susceptible to flooding by a storm surge overtopping or breaching the dykes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".