Validating and improving the Canadian coast guard search and rescue planning program (CANSARP) ocean drift theory
Bibliographic record
Abstract
The Canadian Coast Guard Search and Rescue Coordinator uses a software system to estimate the drift of targets in the ocean, and consequently determine a search area. Existing software applies a simple drift algorithm (MiniMax) that has been in use since World War II (Canadian Coast Guard/Department of Fisheries and Oceans Canada [CCG/DFO], 2000). -- The Coast Guard must be aware of the effectiveness of the drift prediction algorithm, and the efficiency of the environmental inputs used. This thesis determines the practicality of the available methods of MiniMax and the stochastic Monte Carlo approach. In addition, we explore the implementation of higher resolution ocean and sea current inputs. This both improves the current MiniMax algorithm and allows exploration of a modified Monte Carlo approach. -- Using an assembled database of drifting buoys in the North Atlantic Ocean, the accuracy of the MiniMax and the Norwegian Meteorological Office implementation of the Monte Carlo methods are evaluated. Results from the assessment indicate that present prediction methods in CANSARP underestimate actual drifts by 2 to 3 times the actual length. These results are used to determine where improvements must be made to the current algorithms and environmental inputs for eventual application to the search system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".