Improving Shipping Container Damage Claims Prediction Through Level 4 Information Fusion
Bibliographic record
Abstract
Maritime trade accounts for approximately 90% of global trade, and most global supply chains incorporate some form of maritime travel (Cheraghchi et al., 2017). Optimising operations at commercial maritime ports is therefore of significant importance worldwide, and impacts global trade. While damage to vessels and cargo has been studied extensively, as has optimising portside operational efficiency, investigations of damage to shipping containers themselves and the resultant disruption of port-side efficiency remains unstudied. The application of machine learning (ML) techniques to uncover causes of shipping container damage allows for more efficient handling of the service-disruptions they cause, as well as insights into the veracity of the current wisdom held by domain experts in the industry. Further, the application of ML methodologies for dynamic algorithm selection (per level 4 of the JDL/DFIG data fusion model) allows for significant improvements to the overall performance of the aforementioned ML methodologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".