A Sustainable Approach to Support Management Scenarios Related to Infrastructure Construction and Site Remediation in Cold Regions
Bibliographic record
Abstract
In the coming years, the northern regions of Canada and Quebec will be subjected to two significant pressures. On one hand, global warming, already underway, will be felt more significantly in these regions than further south. Furthermore, mining activities included in the Quebec government's "Plan Nord" will become increased. The combination of these two factors creates a major stress on roads, railways, ports and airports in these regions. Although the methods of design and construction on permafrost have improved over time, there remains a source of uncertainty with respect to changes in environmental conditions. Today, the new development perspectives in northern regions represent technical and social adaptation challenges for engineering firms. To better assess all the risks related to the construction and maintenance of infrastructures in cold regions communities, it is necessary to use decision making tools, which are based on the body of knowledge for preventing, where possible, major environmental impacts that could have significant financial and social repercussions. Certain approaches for scenario-base analysis; the multicriteria analysis (Multicriteria Decision Analysis or MCDA) can provide a solid basis for defining the cost-benefits for some construction or maintenance work and also provide the best alternative. Long term sustainable development must also be done with regards to often underestimated human factors. This conference will put into context the use of these tools, based on various northern Canadian projects such These tools form a cornerstone for developing a framework for sustainable management in northern 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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".