Current Address: Specialist, Yard Operations Performance Canadian Pacific Railway
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: 249 words Body: 5,258 words + 5 Figures (1,250 words) + 2 Tables (500 words) = 7,008 words Because railroad classification yards can be considered production systems, insight into the dynamics of a yard system can be gained by adapting production management tools that have led to significant performance improvement in manufacturing. This work focused on improving yard performance by utilizing the concepts of factory physics, Theory of Constraints (TOC) and tools from Lean Manufacturing. The most important manufacturing process analog to improving yard capacity is the bottleneck. In a production system the bottleneck is the process that limits its throughput. As such, the processing rate of the bottleneck sets the rate for the entire system. Improving the performance of the bottleneck is the best way to improve the performance of the entire terminal process. The train assembly (pull-down) process has been identified as the bottleneck in a majority of classification yards. The potential capacity improvement of several bottleneck management alternatives is discussed.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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 it