Supply chain serviceability under climate change with application in the Arctic
Bibliographic record
Abstract
This study evaluates the resilience of Arctic supply chains to climate change by introducing the concept of supply chain serviceability. We define serviceability as a function of vulnerabilities in transportation nodes and modes under disruption threats, focusing on climate change impacts. Using climate data from Northern Canada, we assess serviceability under three Shared Socioeconomic Pathways (SSPs): SSP1-2.6 (low emissions), SSP2-4.5 (moderate emissions), and SSP5-8.5 (high emissions). We use Monte Carlo simulations to predict climate-induced impacts on six airports and three aircraft types. The detailed analysis of Yellowknife and Iqaluit airports and military aircraft validates our methodology. We include the results for additional airports and aircraft types in the e-companion. Our findings indicate average serviceability index declines of up to 51 permafrost degradation, extreme weather events, and infrastructure vulnerabilities. Our study provides actionable managerial insights and theoretical contributions to support supply chain resilience initiatives. • Serviceability framework to evaluate supply chain resilience under climate change. • Methodology to calculate serviceability index using climate projections. • Arctic Canada case study to illustrate practical implications of the framework. • Insights to guide resilient supply chain strategies and infrastructure planning. • Theoretical and empirical opportunities to extend the proposed framework.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".