Improved Model of Deep-Draft Ship Squat in Shallow Waterways Using Stepwise Regression Trees
Bibliographic record
Abstract
To maintain an optimum balance between security and efficiency of maritime transport in shallow waterways with a lot of deep-draft ship traffic such as in the St. Lawrence Waterway, it is particularly important to accurately estimate the ship squat, which is the reduction of the underkeel clearance between a vessel at rest and in motion. Recently, a squat model based on a regression tree was developed. The skill of this model to predict squat in the St. Lawrence Waterway exceeded the performance of 10 empirical models commonly used by the operational and regularity agencies. Although this approach is promising, two main problems were noticed: (1) the predictions obtained by the regression tree are not smooth and (2) the squat predicted with this model is not always monotonically increasing with ship speed (Froude number). In this paper, a stepwise regression tree algorithm is used to model squat. This approach has the same advantages as the regression tree (allowing the representation of complex and nonlinear relationships) and solves both of the aforementioned problems. Furthermore, the squat predictions of the new stepwise regression model outperform the predictions of the regression tree model and the Eryuzlu model, which is currently used by the Canadian Coast Guard. This new model could provide a handy tool for mariners to get real-time squat predictions in the St. Lawrence River. We also provide an algorithm that can be used to fit a squat model for any other economically important shallow waterway.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".