Sustainable intermodal freight transportation : applying the Geospatial Intermodal Freight Transport model
Bibliographic record
Abstract
To study the energy and environmental impacts of emissions associated with freight transportation, the Geospatial Intermodal Freight Transport (GIFT) model was created as a joint research collaborative between the Rochester Institute of Technology (RIT) and the University of Delaware (UD). The GIFT model is a Geographic Information Systems (GIS) based model that links the U.S. and Canadian water, rail, and road transportation networks through intermodal transfer facilities to create an intermodal network. The purpose of my thesis is to apply the GIFT model to examine potential public policies related to intermodal freight transportation in the Great Lakes region of the United States. My thesis will consist of two papers. The first paper will examine the environmental, economic, and time-of-delivery tradeoffs associated with freight transportation in the Great Lakes region and examine opportunities for marine vessels to replace a portion of heavy-duty trucks for containerized freight transport. The second paper will explore the potential benefits of using the Great Lakes as a corridor for short-sea shipping as part of a longer intermodal route. The intent of my thesis is to shed light on the current issues associated with freight transport in the Great Lakes region and present public policy alternatives to address said issues. Ideally, this thesis will better inform policymakers on the impacts and tradeoffs associated with freight transportation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".