Smart Iceberg Management System – Rapid Iceberg Profiling System
Bibliographic record
Abstract
Abstract High resolution iceberg profiles are an essential element of an intelligent ice management toolkit. This paper describes field work undertaken during the spring and summer of 2015 to test our high resolution, rapid iceberg profiling system and presents some key results obtained. The profiling system uses a multibeam SONAR for the iceberg keel and a LIDAR for the iceberg sail. The system was used to collect 10 different iceberg profiles in the waters off eastern Newfoundland, ranging in size from 20m to 190m (waterline length). Profiling was performed at a speed of up to 6kts, allowing a 100m (waterline) iceberg to be profiled in under five minutes. The system is able to collect data even when significant vessel roll/pitch is evident and is able to compensate for iceberg movement during the profiling operation. Iceberg profiles created by C-CORE's system are validated by comparison with photographs and also via hydrostatic analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".