DATA INTEGRATION AND VISUALISATION REQUIREMENTS FOR A CANADIAN MARINE CADASTRE: LESSONS FROM THE PROPOSED MUSQUASH MARINE PROTECTED AREA
Bibliographic record
Abstract
The coming into force of the United Nations Convention on Law of the Sea (UNCLOS) has forced the subdivision of the oceans into Territorial Seas, Exclusive Economic Zones and Continental Shelves, each with its attendant right and responsibilities. As it explicitly deals with the rights, restrictions and responsibilities to the physical offshore, UNCLOS has created a complex multidimensional mosaic of potential private and public interests. When coastal zone management programs, and internal jurisdiction and administration issues are added on, a clear understanding of the nature and extent of offshore interests is crucial for decision-making purposes. One such coastal zone management program is the Marine Protected Area ∗ (MPA) program in Canada. This paper reports on one of the objectives of the “Good Governance of Canada’s Oceans” project: To highlight data integration and visualisation challenges in visualizing the complexity of rights in marine spaces. Specifically, this paper reviews the technical challenges of data integration and visualisation that were encountered as part of a case study involving the proposed Musquash MPA. These technical challenges are particularly important considering the spatial data scale, format, precision and accuracy issues intertwined with the jurisdiction and administrative uncertainty found in Canadian marine space. It is in this context that the authors view these technical challenges as synonymous with those to be encountered in building a marine cadastre.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".