CanCoast 2.0: data and indices to describe the sensitivity of Canada's marine coasts to changing climate
Bibliographic record
Abstract
Baseline mapping of coastal characteristics and understanding of the dynamic response of coastal sensitivity to environmental changes provide a strong foundation for climate change adaptation in Canada's coastal regions. CanCoast is a collection of datasets that describe the physical characteristics of Canada's marine coasts. It includes datasets that are not expected to change through time (such as coastal materials and backshore slope), and some that are projected to change as climate changes (such as wave height and mean sea level). CanCoast includes: sea-level change (early and late 21st century); wave-heights including the effects of sea ice (early and late 21st century); ground ice content; coastal materials; tidal range; and backshore slope. These are mapped to a common high-resolution shoreline and used to calculate indices that show the generalised coastal sensitivity of Canada's marine coasts in early and late 21st century climates, and the spatially-variable change in sensitivity between the early and the late 21st century. Because of the scales of the input data, the generalised indices are best used to identify regions that differ in sensitivity to changing climate, rather than local properties or coastal infrastructure with specific characteristics that cannot be resolved in this national-scale approach.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".