Bibliographic record
Abstract
The breadth of both of the Canadian railway system networks, along with their ability to adapt their track renewal and upgrade plans to meet growing traffic patterns, will allow them to tap into emerging markets. The Canadian National Railway Company (CN) uses current traffic volume and future growth to determine the needs of their rail infrastructure. They have developed a highly efficient supply chain that connects frac sand producers in Wisconsin with fast-growing oil and gas shale basins in the U.S. and Canada. In order to accomplish this, it is upgrading two branch lines. The first is the Barron Subdivision that was transformed from an out-of-service 80-pound line to a 286,000-pound car capacity line. The second is to upgrade the Whitehall subdivision which will allow CN to handle 286,000-pound loads along 74 miles. Canadian Pacific (CP) has shown a progressive increase in growth, from 500 carloads in 2009 to 53,500 carloads in 2012 with estimates to move 70,000 carloads in 2013. The article discusses how CP has taken steps to maintain the track infrastructure, investing over $96 million over regular maintenance programs to upgrade the Bakken network. CP has recently announced a $1.16 billion program to enhance their North American network to meet growth in oil by rail and other business lines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".