Applications of Coastal and Engineering Geology in the Identification, Prediction and Adaption to Geohazards in Nova Scotia
Bibliographic record
Abstract
A variety of geohazards are identified, but examples of major hazards include processes such as shore face erosion, coastal flooding, rock falls, large scale rotational slumps, beach deflation and migration. The spatial occurrence and magnitude of the impact of geohazards is highly variable in Nova Scotia. Many factors influence this variability including bedrock type and structure, the physical presence or absence of exposed bedrock, sediment supply, near-shore water depth, coastal geomorphology, and variable exposure directions to different storm track patterns. There is a myriad of other factors that locally influence the type and the magnitude of risk associated with coastal geohazards. The division’s Coastal Hazard Assessment Project has three main focuses: (1) the identification and quantification of hazard or risk, (2) determining the type of risks found in the many and diverse coastal environments in Nova Scotia and (3) examining both public and private coastal infrastructure from a diverse scientific perspective to assist in determining the best ways to mitigate risk and enhance the sustainability of coastal infrastructure. One specific example is the use of engineering geology to identify and mitigate effects such as coastal erosion and wave impact on private and public land and infrastructure. The last year’s activity has concentrated on the examination of coastal provincial parks and efforts to reduce the cost of storm damage and to increase the sustainability of coastal park infrastructure (Fig. 1).
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".