Fuel consumption for double-stack intermodal trains
Bibliographic record
Abstract
This report paper offers the reader an outline of the development of a MLR model to predict fuel\nconsumption for double-stack intermodal trains in Canada. Results suggested the necessity to\nclassify the data in a non-finished project and design categories for various factors. Furthermore,\nthe conclusion extracted is the requirement to compose small-scale models for specific\nconditions of track and train in order to obtain the results for an entire railroad section.\nWhereas the lack of success on the original goal of this research, which was to predict fuel\nconsumption for intermodal trains in any random configuration under any condition of railroad\ntrack, the scope of the project was shortened to a very specific piece of section of the Canadian\nrailway network for trains that travel full power. This was as a result of complications disclosed\nduring the development of the project and troubles discerning the vast data
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".