Validation of a KPI Framework Demonstrating Economic Benefits Gained through Resolving Nautical Bottlenecks on the Danube
Bibliographic record
Abstract
Addressing nautical bottlenecks is crucial to optimize the utilization of inland waterways and maximize economic benefits of transports. To maximize economic benefits, a study was conducted to validate a Key Performance Indicator (KPI)-based framework. This framework offers a structured approach to assess the impact of resolved nautical bottlenecks on the economic benefits of inland waterway transport (IWT). To validate the applicability of the KPI framework, interviews with eleven experts were conducted. The goal was to prioritize each KPI based on their insights. The results of the interviews shed light on the relevance and coherence of both the individual KPIs and the overall KPI framework. The experts confirmed the importance of measures related to transportation efficiency, such as reduced transit times, increased vessel throughput, and enhanced reliability. The validated KPI-based framework serves as a valuable tool for policymakers, industry stakeholders, and researchers. It enables the assessment of the effects of resolving nautical bottlenecks in inland waterway systems. Future research should focus on quantifying the multifaceted impacts, making this framework even more useful for decision-making processes concerning investments in infrastructure upgrades and maintenance.
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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 | 0.054 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".