Case study scenarios in site selection of hazardous material facilities based on transportation preferences
Bibliographic record
Abstract
A methodology is proposed to evaluate and rank potential sites for facilities dealing with hazardous materials (HAZMAT). The proposed methodology incorporates HAZMAT route planning into facility siting while considering transportation preferences and challenges. The area of interest is divided into smaller zones representing potential sites for a HAZMAT facility. A multimodal transportation network including railways and roads is considered for transportation of HAZMAT. Each zone is evaluated based on its accessibility from a set of selected points of interests (POIs), which are defined as potential origin/destination points for transportation of HAZMAT. The shortest routes between each POI and potential zones are evaluated based on a cost function which can accommodate multiple criteria to determine the associated disutility for each potential zone. Finally, zones are ranked based on their cumulative disutility scores. The proposed analysis method is quantitative, and at the same time it is adequately flexible to allow inclusion of subjective criteria. Application of the proposed methodology is demonstrated for identifying optimal locations for a HAZMAT facility (e.g., a nuclear facility) using the Canadian province of Saskatchewan as an example. Three scenarios were evaluated including (1) all network segments and POIs were treated equally, (2) network segments were rank ordered based on their functional classification while POIs were treated equally and (3) network segments were rank ordered based on their functional classification with preferences given to specific POI(s).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".