<title>Ice images processing interface for automatic features extraction</title>
Bibliographic record
Abstract
Canadian Coast Guard has the mandate to maintain the navigability of the St.-Lawrence seaway. It must prevent ice jam formation. Radar, sonar sensors and cameras are used to verify ice movement and keep a record of pertinent data. The cameras are placed along the seaway at strategic locations. Images are processed and saved for future reference. The Ice Images Processing Interface (IIPI) is an integral part of Ices Integrated System (IIS). This software processes images to extract the ice speed, concentration, roughness, and rate of flow. Ice concentration is computed from image segmentation using color models and a priori information. Speed is obtained from a region-matching algorithm. Both concentration and speed calculations are complex, since they require a calibration step involving on-site measurements. Color texture features provide ice roughness estimation. Rate of flow uses ice thickness, which is estimated from sonar sensors on the river floor. Our paper will present how we modeled and designed the IIPI, the issues involved and its future. For more reliable results, we suggest that meteorological data be provided, change in camera orientation be changed, sun reflections be anticipated, and more a priori information, such as radar images available at some sites, be included.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.574 | 0.398 |
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".