Defining and Adapting to Coastal Hazards in Atlantic Canada: Facing the Challenge of Rising Sea Levels, Storm Surges, and Shoreline Erosion in a Changing Climate
Bibliographic record
Abstract
The coastal zone, comprising the intertidal zone and a band extending miles landward and seaward of the shoreline, is an area of constant dynamic change. It encompasses some of the world’s most valuable resources, both natural and economic. Coastal hazards include storm-surge flooding, storm-wave run-up, ice ride-up or pile-up, coastal slope failure, and shoreline erosion, among others. It is important to determine the potential for changing hazard conditions under existing rates of relative sea level rise and the possible effects of warming climate, including accelerated sea level rise, increased storminess, reduced sea ice, and other factors. Appropriate design, land management, and risk reduction depend on an adequate understanding of the hazards and realistic mapping of hazard zones. This paper describes how the coastline can no longer be considered as merely a line on paper, it should be understood as a three-dimensional landform that is subject to physical change over time. Coasts, especially those in high energy zones, can be difficult and very costly to map accurately. Recently developed remote-sensing techniques such as airborne laser altimetry provide a new capability to produce high-resolution digital elevation models of low-lying flood-prone terrain in support of other innovative approaches to risk reduction and hazard mitigation. The paper describes how recent technological development s have led to significant advances, including the evolution of geographic information systems (GIS), electronic charts, global positioning systems (GPS), high resolution multibeam swath bathymetry and airborne laser altimetry (LIDAR). Using remotely sensed data at low tide, on can acquire continuous coverage of the intertidal zone, allowing a seamless merger of land topography with bathymetric data. Potential coastal flood areas can also be accurately determined with high-resolution digital elevation modes acquired by airborne and spaceborne remote sensing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".