Real-time classification of the sea ice interacting on a bridge pier using artificial intelligence techniques
Bibliographic record
Abstract
This research focuses on the development of a novel approach for monitoring and classifying different kinds of sea ice interacting with bridge piers in Northumberland Strait. Different ice types can have a varying impact on the navigability of a vessel or the loads on a structure. As such,the ability to monitor and classify different ice types automatically is an added strength to the existing ice load monitoring system present at the Confederation Bridge since 1997 and should allow for further automation. A deep learning algorithm based on Convolutional Neural Networks (CNN) has been utilized for training an algorithm to classify four different types of sea ice interacting with the bridge pier. Despite the fact that classification of different ice types from images is sometimes very challenging even when performed manually by experts, the developed algorithm is able to identify different classes accurately and on a real-time basis. The accuracy of the results demonstrates high practical utility of the method for similar applications.25thIAHR International Symposium on Ice Trondheim, June 14 to 18, 2020.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".