ASSESSING LAND SUITABILITY FOR RESIDENTIAL DEVELOPMENT IN PERMAFROST REGIONS: A MULTI-CRITERIA APPROACH TO LAND-USE PLANNING IN NORTHERN QUEBEC, CANADA
Bibliographic record
Abstract
Northern Quebec (Nunavik) presents an important intersection between population growth and climate change. The Inuit population of Nunavik has the fastest growth rate in any region of Canada. Land-use planning is an urgent and pressing need for northern communities built on permafrost, where there are considerable risks to development in areas where permafrost may thaw. As northern communities work to adapt to climate changes, they will be in great need of confident recommendations about locations of future development projects. This paper presents a case-study of the community of Tasiujaq and assesses the probability of thaw settlement of the surface, a process seriously affecting infrastructure stability. A method is developed for quantifying uncertainty in the resulting map, expressed as a function of judgmentbased uncertainty in the various factors that can influence eventual map quality. The best estimate of vulnerability and of the confidence in that estimate can be expressed in a single, simple map that allows an analyst to convey both of these vital aspects of the assessment process.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".