A study to assess the applicability of using remote sensing to minimize service interruption of Canadian port infrastructure
Bibliographic record
Abstract
Abstract Remote sensing can be an effective tool for providing early warning of deformations that are difficult or impossible to detect with visual inspections, and can allow infrastructure owners and operators to prioritize key areas for maintenance and assist in more rapid recovery from incidents that cause service interruptions. Canadian Port Authorities (CPAs) do not currently use remote sensing techniques to assess the condition of critical infrastructure despite their application to similar operations such as bridges, pipelines, and dams. Interviews with key informants indicate that there are no technical or scientific barriers to using remote sensing to assess port conditions, and that while the presence of heterogeneous materials and surfaces, restricted air space, and a complex web of interconnected port partners and tenants complicates its use, these are surmountable. Instead, interviews suggest that the primary barrier stopping CPAs from implementing remote sensing is that there is no system-wide formal chain of responsibility and authority and a lack of public sector agency leadership resulting from the commonly used “landlord model” for port operations, which generates fragmented stakeholder interests and inhibits collaboration. Given the emerging challenges of aging infrastructure, climate change-related impacts, and the demands of system-wide business continuity, we argue that a private and public sector collaborative pilot project is needed to test the feasibility of adopting remote sensing for port infrastructure assessment.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".