Multi-Criteria Analysis with Geographic Information Systems in Changing Permafrost Environments: Opportunities and Limits
Bibliographic record
Abstract
For northern regions underlain by permafrost, the projected increase in temperatures will greatly impact the physical properties of soils and their capacity to support infrastructure. In this context, the need to identify land parcels to build new housing in a safe and sustainable manner is pressing in Nunavik (northern Quebec, Canada). To guide residential development in these particular settings, this paper presents a GIS-based approach developed to identify terrain sensitivity to thaw-settlement and mass movements, geomorphological processes responsible for costly readaptation or relocation of infrastructure. Land-suitability assessment for residential development in permafrost regions requires the consideration of many factors that may interact in complex ways. Combining factors to assess suitability presents a number of challenges that are especially important to resolve in permafrost-region studies to guide fine-scale decisions. Using an example of a GIS system built for the village of Tasiujaq in Nunavik, this paper illustrates some of the specific challenges to the effective use of GIS for management in permafrost regions. Temporal, existential and representational limits are inherent to the use of GIS and important to consider in land planning for permafrost studies. This paper also demonstrates how permafrost science can benefit from GIS technologies, especially their capacity of adaptation and transformation to any scenarios and changes in environmental parameters.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".