Les données de recherche géospatiales au Canada: un survol des projets régionaux
Bibliographic record
Abstract
Researchers from a broad array of disciplines depend on Canada's federally-produced geospatial data and digitized cartographic materials to provide information on environments and changes over time.• The use of these products in foundational and transformational research is hindered because many of them are not FAIR.Researchers are unable to find, access, and use the resources they need for their research.• Collections of these materials are distributed across a variety of stewarding government agencies, libraries, and archives.They vary significantly in their discoverability, description, availability, and format-compatibility for modern analytical approaches.• Canada needs to invest in robust and enduring DRI that preserves these unique and invaluable collections, and enables their broad discovery and reuse in research.• National-level coordination is required so that distributed stewarding organizations can work together to inventory these materials, describe them, and where needed, digitize and transform them into research-ready products.• A national DRI strategy should facilitate collaboration between data users, producers, stewards, and providers to develop a centralized clearinghouse for these resources, as well as the underlying standards and technical infrastructure that will make these materials FAIR for future generations of researchers.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".