Canadian Geospatial Data Infrastructure, architecture description
Bibliographic record
Abstract
The Canadian Geospatial Data Infrastructure (CGDI) is a distributed set of data, as well as services and applications that enable the sharing and use of geospatially referenced information. The CGDI is being developed by the Geoconnections program. A complete introduction to the CGDI and its various aspects is found in the CGDI Target Vision. CGDI is an open information technology infrastructure that is based upon publicly available specifications. The architecture is designed to enable the implementation of systems to support service providers, data providers and application developers, using interoperable and re-usable components. This goal is achieved largely through specifying the interfaces of these services. These specifications draw upon the International Organization for Standardization (ISO) 19100 series of abstract standards for Geographic Information, and related implementation specifications under development by the Open GIS Consortium (OGC). This architecture description document is one of a trio of evolving documents that describe the CGDI: 1. The CGDI Target Vision, 2. The CGDI Architecture Description, and 3. The CGDI Implementation Plan.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.021 |
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".