Open data records published to support marine spatial planning in Pacific region
Bibliographic record
Abstract
Marine spatial planning (MSP) is the process of managing ocean spaces sustainably to meet ecological, economic, cultural, and social objectives. Federal, provincial, and Indigenous governments are working with stakeholders to advance MSP in five planning areas across Canada. In the Pacific Region, this work focuses on the Northern Shelf, Strait of Georgia, and Southern Shelf bioregions. DFO Science staff have worked with data custodians and researchers to develop an inventory of ecological data, create new spatial data products, and support the publication of spatial data highly relevant to MSP. Priority for data creation and publication was given to ecological datasets that helped fill longstanding data gaps for different species, habitats, and environmental parameters, data that are comprehensive for the Pacific coast, and current and long-term data products. The resulting spatial datasets were made available through the Government of Canada’s Open Data platform. This technical report contains a summary of the spatial datasets developed with support of MSP staff and published to Open Data. The report is organized by ecological features, oceanographic datasets, and human activities. For each dataset, details are provided from the metadata, including an overview of the dataset, methods, data sources, and uncertainties, and maps of each associated data layer.
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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.069 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.030 |
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".