CLIMATE CHANGE AND TRANSPORTATION: POTENTIAL INTERACTIONS AND IMPACTS
Bibliographic record
Abstract
The purpose of this paper is to illustrate several potential interactions between anthropogenic climate change and transportation based on published literature and the opinions of the authors. Although the content is synthesized primarily from Canadian research, most of the transportation-climate relationships are equally relevant to the American context. Unfortunately, the available peer-reviewed literature addressing this subject is very limited. Although there is no comprehensive quantitative assessment of the various transport-sector costs and opportunities associated with the current, let alone changed, climate, there are several qualitative summaries describing the vulnerabilities of transport-related activities to climate variability and change. In addition, a few quantitative impact analyses of climate change on selected transportation infrastructure and operations have been published. However, the bulk of literature relevant to climate change deals with current weather and climate sensitivities of transport systems. Weather and climate contribute to several hazards or sensitivities within the transportation sector (such as landslides, reduced visibility, etc.). The statistics of these variables may be affected by anthropogenic climate change. Weather and climate factors directly affect the planning, design, construction and maintenance of transportation infrastructure in several ways--they also indirectly affect the demand for transportation services. Costs and benefits, measured in terms of safety, mobility, economic efficiency, and externalities, accrue as the operation of transportation facilities and services meets these demands and adjusts to weather and climate hazards. The remainder of this paper explores some of the conceptualized climate-transportation interactions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".