Adapting to the risks and uncertainties posed by climate change on ports
Bibliographic record
Abstract
Climate change has become a critical issue in port supply chains in recent decades, involving a variety of disciplines and posing substantial challenges to ports due to their high vulnerability. To date, there is insufficient research on how to minimize these uncertainties in terms of decision-making and port planning. Also, even for port operators who have taken countermeasures to minimize the impacts of climate change on their ports, some strategic and planning problems still remain. Based on the above issues, this thesis proposes that it is pivotal to enhance the awareness of the community’s consideration of the risks and uncertainties of climate change impacts on ports, and calls for adaptation strategies to cope with climate change impacts from the perspective of port supply chains. Through an extensive literature review, and a nation-wide survey, as well as in-depth interviews in case studies focused on a seaport, an inland port and railway (Port of Montreal, CentrePort Canada and Hudson Railway respectively), this thesis provides and overview of the risks and uncertainties posed by climate change to Canadian ports. Through both quantitative (SPSS in survey) and qualitative analyses (interviews in the case study), it is expected to fill the gaps of regional studies focused on Canada and the under-researched areas including dry ports, port supply chains and adaptation port planning by considering the risks and uncertainties posed by climate change.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".