Tra-940: Assessing Current and Future Mackenzie River Freight Volumes in the Context of Climate Change Impacts
Bibliographic record
Abstract
The Mackenzie River is a major freight transportation route that connects many remote communities in the Northwest Territories and parts of Nunavut to southern Canada’s transportation network. The river is only navigable during the summer months, from mid-June until sometime in late-September to mid-October, when it is clear of ice. However, the water conditions of the river have changed significantly in recent years. Although water levels always decrease as the delivery season moves into fall, these reductions have been occurring much faster, in turn reducing barge loading capacities as well as operational speeds. In addition, based on simulations of ice breakup and water volumes in the Mackenzie River basin, the sailing season opening dates are anticipated to shift earlier in the future. In the end, the main impact of climate change on river transport is not definitive events but rather, increased variability in events. This research aims to account for those abovementioned climate changes in the freight volume scheduling process, and conducts a numerical analysis based on the projections of future water conditions from climate simulation models as well as predicted freight volumes from time-series analysis and forecast models. The results of the numerical analysis can help local government and waterway transportation companies to better understand how freight scheduling strategies could account for climate changes that affect regional waterway transportation and, hence, optimize their operational schedules to take advantage of good water conditions while reducing financial cost.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".