An Application for Regional Coastal Erosion Processes in Urban Areas: A Case Study of the Golden Horseshoe in Canada
Bibliographic record
Abstract
Urban growth has had unprecedented consequences on environmental sustainability and anthropogenic activity. The eroding coastlines throughout the world are subject to the massive expansion of urban areas and the accountability of sustainable hinterland landscapes. The Golden Horseshoe is Canada’s fastest growing region extending from the Niagara Peninsula and one of the most active economic regions in North America. This paper adopts a combined assessment of land use change and transitions in the coastal stretches of the Greater Golden Horseshoe. Comprising the urban expansion of the region between 1990 and 2011, an integrated assessment was carried out to: (i) detect changes in coastal lines along Lake Ontario; (ii) derive land use changes along the coast through spatial accounting matrices; and (iii) integrate climate change data for a combined assessment of future erosion loci. Visible erosion was found between the decade of 1990 and 2000, while certain areas have shown coastal recession in the southern region. The maximum recession was found to be 30 m, with an increasing urban sprawl of 19.8% between 1990 and 2000. A combined temperature increase of 2 °C over the coming decades brings the increase in urban heat islands leading to the importance of combined land policies to mitigate the common problem of erosion in vulnerable urban stretches and liveability concerning spatial resilience of growing urban regions in North America.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".