Improved projections of sea-level change and nuisance flooding in Atlantic Canada: The importance of GIA-induced land motion
Bibliographic record
Abstract
The socio-economic impacts of sea-level rise are significant, especially in coastal regions with dense populations and costly infrastructure. Accurate projections of sea-level changes at regional scales are essential for risk assessment but are challenging due to the interplay of processes affecting the height of both the land and sea surface (and, therefore, relative sea level). Rising sea levels from ice melting and ocean expansion exacerbate flooding risks, with nuisance flooding serving as an early warning for vulnerable regions such as Atlantic Canada, which is experiencing GIA-induced land subsidence. The compounded effects of GIA and contemporary sea-level rise escalate regional vulnerability to flooding. This study improves projections of mean sea-level changes and nuisance flooding in Atlantic Canada by integrating the sea-level signal from optimal regional GIA models into the framework adopted in the 6th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Projections under SSP1-1.9, SSP3-7.0, and SSP5-8.5 scenarios for 2050, 2100, and 2150 CE are used to assess nuisance flooding frequency at 40 tide gauge stations. Our results demonstrate that the GIA signal contributes significantly to flooding frequency estimates and that these estimates can depart considerably from those estimated using the IPCC (AR6) mean sea level projections. For example, nuisance flooding at Halifax becomes chronic (>50 days annually) by 2050 CE under SSP3-7.0 using our GIA model results. This level of chronic flooding occurs in Halifax at 2050 CE only for the most extreme scenario (SSP5-8.5) when using the IPCC mean sea level projections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".