Bibliographic record
Abstract
The marine environment is our last frontier. The need for better data and information is paramount to ensure proper management and sustainable development of this remote, often inhospitable and multi-dimensional environment. New technological advances in our ability to collect and process ocean data have resulted in very rapid growth in the volume of data and information about this environment. Efforts are now underway in many jurisdictions to implement the technical and policy framework required to facilitate access to this geospatial data and information. In Canada, government and industry are collaborating to develop a Canadian Geospatial Data Infrastructure (CGDI) through a national program called GeoConnections. As partners in this initiative, the Canadian Centre for Marine Communications (CCMC) and the Department of Fisheries and Oceans (DFO) are leading the development and implementation of a Marine Geospatial Data Infrastructure (MGDI) that will facilitate access to inshore, coastal and marine environmental, transportation and resource data and information by a broad range of users. This paper outlines the building blocks required to implement MGDI and
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.816 | 0.700 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".