Analyzing Glacial Isostatic Adjustment Along the Coast of Nain, NewFoundLand in Relation to Sea Level Rise
Bibliographic record
Abstract
Glacial isostatic adjustment (GIA) is the response of the Earth’s crust, the Earth’s gravitational field, and the worlds oceans to the thickening and thinning of global ice sheets (Whitehouse, P., 2018). GIA has been an ongoing geomorphological process across North America, influencing the Atlantic coast of Canada in relation to sea level rise (SLR). The primary driver of SLR is the melting of land-based ice sheets and mountain glaciers, contributing approximately 2 mm per year to global sea level rise (Lindsay, R., 2023). This study aims to determine whether the rate of glacial isostatic adjustment along the coast of Nain, Newfoundland will outpace the projected sea level rise by 2150. Through analyzing future SLR projections and GIA rates, the extent to which vertical land motion counters sea level rise will be analyzed. Sea level measurements were obtained using satellite altimetry data from the TOPEX, Jason-1, Jason-2, Jason-3, and Sentinel-6 satellites. These measurements do not account for the vertical movement of the Earth. Using projections from satellite-based SLR data, four simulation models were developed for the years 2000, 2050, 2100, and 2150. These models were run using Google Earth Engine through integrating rates of GIA for the city of Nain, approximately 4 mm per year, and projected SLR data from the satellite altimetry data. Comparisons will be made to the city of Umiujaq, Quebec, along Hudson’s Bay, which is expected to have similar increases in SLR, however, is experiencing GIA rates of approximately 9 mm per year. I will compare the predicted rates of SLR in relation to GIA to determine the extent of flooding along the coast of Nain for the years 2100 and 2150. Preliminary results suggest that if the rate of GIA is outpaced by SLR projections, coastal flooding risks will increase, necessitating adaptive strategies for vulnerable regions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".