Expanding Alaska–Canada Rail
Bibliographic record
Abstract
Recent changes in global markets have raised the value of mineral resources in northwestern Canada and Alaska. The development of these resources depends on the economics of rail infrastructure expansion. Transportation decision makers need revenue and cost assessments to plan investment in rail infrastructure. A tool based on a geographic information system was developed for mineral resource evaluation and visualization. The tool incorporated expert-augmented mineral resource data for more than 22,000 occurrences in the region. The tool included the proposed Alaska–Canada Rail Link, which would connect Alaska rail to the lower 48 states. Users selected locations of known mineral occurrences near actual or proposed rail routes and used statistical mineral deposit models to estimate resource sizes and extractable value over time by combining current or user-entered commodity prices with multimodal revenue freight volumes and optimally routed transportation costs. The tool translated the revenue scenario into likely carbon dioxide emissions according to the transport of mineral concentrates to regional and international destinations. Users could select and visualize multimodal transportation networks to understand and minimize mobile-source carbon emissions as part of their scenarios. Statistical estimates of mine capital expenditure and operating costs were also calculated according to type. The tool calculated the gross metal value of a mineral occurrence with statistical deposit models. This index was linked to the positive regional economic impact associated with the developed resource in terms of jobs, taxes and royalties, and resupply. This information helped decision makers close the loop on infrastructure investment assessments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".