People at the tidal flats: coastal morphology and \nhazards in Iqaluit, Nunavut
Bibliographic record
Abstract
Rapid environmental change observed in the Canadian Arctic is driving efforts at \nfederal, territorial, and municipal levels to adapt to the impacts of projected changes. \nRecent work with communities has shown that targeted and relevant scientific input \ncan greatly enhance ongoing vulnerability assessments and policy planning around \nadaptation and sustainability. The Arctic coast is dynamic, creating risk to Arctic \ncoastal infrastructure. Using GIS modelling and geoscientific data collected over three \nfield seasons, this thesis reports on a project aimed at providing coastal hazard mapping \nfor Iqaluit, Nunavut. Iqaluit is the capital city of Nunavut, and sits alongside \na macrotidal embayment with extensive tidal flats, which influence many aspects of \nlife in the community. Data collected include: detailed topography and bathymetry, \nelevations of the coastal setting, elevations of past extreme water levels, and morphological \nmapping. The results build on previous work in Iqaluit, showing a relatively \nstable boulder-strewn sand flat morphology in the macrotidal embayment. Modelling \nof the coastal topography indicates a recent (last century) period of quasi stable sea \nlevel, with possible slight emergence persisting. Hydrodynamic data reveal little evidence \nfor significant erosion through wave and current input. Recorded nearshore \ncurrent velocities were between 0.1 - 0.3 m/s, with greater velocities at the top 3 m of \nthe water column. The hazard mapping then attempts to incorporate the morphological \nmapping into a GIS of coastal infrastructure in the city in order to provide detailed information for city planners. Results show limited freeboard of 0.3-0.8 m for most \ncoastal infrastructure under an upper-limit projection of 0.7 m relative sea-level rise \nfrom 2010 to 2100. Key infrastructure, and especially the subsistence infrastructure \nfocused on the coast, is actually below past recorded maximum water levels during \nhigh spring tides. Lack of data, however, precludes any reasonable estimate of recurrence. \nGeomorphological mapping of the coastal setting provides crucial insight \ninto the risks to infrastructure from storm waves, erosion, and sea-level rise. The \nstudy shows that the tidal flats are a source of coastal resilience in the form of wave \ndissipation, lowering ice pile-up/ride-up risk, and protection from rapid erosion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".