Canada’s coastal dynamics from multi-decadal Landsat
Bibliographic record
Abstract
An existing national-scale Landsat dynamic surface water dataset over Canada is leveraged to map coastlines in 5-year periods centred on 1987, 2003 and 2019. The spatial and temporal consistency in shoreline locations mapped due to tide level variation is verified using simulations with tide gauge data, and high-resolution WorldView-2 scenes are used to benchmark shoreline positional accuracy. Shoreline change is mapped between periods, and erosion, accretion, and stability are calculated for Canadian Arctic and southern coastal regions. Simulations indicate that the variance in mapped shoreline position due to random sampling of clear-sky Landsat pixels is similar or less than that of tide modelling used in other studies. The shoreline positional accuracy is within one Landsat pixel with a consistent seaward bias, which is comparable to benchmark results for Landsat shoreline extraction algorithms over a micro-mesotidal site with a wide intertidal zone. Between 1987 and 2003, accretion dominated in the Arctic and erosion in southern Canada. While erosion continued to dominate between 2003 and 2019 in southern regions, the Arctic region switched from being accretional to net erosional, possibly due to climatic effects overtaking isostatic rebound. A comparison of the derived coastal dynamics and the CanCoast Sensitivity Index (CSI), which represents an aggregate measure of coastal physical susceptibility to climate change, shows strong monotonic relationships in both Arctic and southern study regions, confirming the consistency between both datasets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".