Geodisy : New geospatial data discovery for Canadian research data
Bibliographic record
Abstract
Presentation for Open Repositories 2021. With the rapid proliferation of research data, it is vital to create innovative tools for data discovery and access. This is the goal of Portage’s Geodisy project, an open-source spatial discovery tool for Canadian interdisciplinary open research data. Geodisy provides a map-based search available alongside the Federated Research Data Repository (FRDR), a national discovery layer indexing over 70 Canadian open repositories. Geodisy is intended for users with diverse experience levels and subject interests and is designed to be accessible for those without GIS knowledge. Data and metadata are extracted from the native repositories and are discoverable based on their location, and individual geospatial files are previewed as visual overlays. For any research that relates to geospatial location, this tool provides a new and useful form of visual discovery.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 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".