Bibliographic record
Abstract
The Canadian Arctic currently faces changing coastlines due isostatic rebound and climate change-driven sea-levels rising. This thesis seeks to answer how local coastlines will change over time under different Representative Concentration Pathway (RCP) scenarios, where errors in modelled coastlines come from, and how much of an impact isostatic rebound has on sea-levels compared to climate-change driven changes. Maps which show changes in coastline through predicted sea-level changes in 2035, 2065, and 2100 have been produced through ArcGIS Pro’s Forest-based regressions tool, with training data from historical climate variables and projection data under CMIP5 for RCP 2.6, 4.5, and 8.5, where RCP 2.6 refers to a low emissions scenario, RCP 4.5 refers to a moderate emissions scenario, and RCP 8.5 refers to a high emissions scenario. Isostatic rebound data can be sourced and run through the ICE-4G model. Multiple models were examined to ascertain which climate variables are needed to produce a stable model, using training data prior to and including 2000 and testing data post 2000 from 9 spatially diverse locations in the Canadian Arctic. These results suggest that for Cambridge Bay, Resolute, and Alert, there will be little change in coastline under any RCP scenario. However, for Tuktoyaktuk coastlines are projected to advance significantly. While 9 locations total were used in during the process, only 4 were examined in greater detail due to data and time constraints. These finding should be considered when making plans for future coastal infrastructure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".