GIS Collaborations in Saskatchewan: SGIC and the University of Saskatchewan Library
Bibliographic record
Abstract
GIS (Geographic Information System) libraries face challenges at both ends of the spectrum when it comes to acquiring GIS data. On one hand, the increase in popularity of GIS driven by services like Google Earth, Bing maps, and open data has made large amounts of GIS data freely available to users. On the other hand, specialty GIS data products, often needed by researchers, industry, and government, can be so costly that they are impossible for a library to purchase on its own. In situations like the latter, collaborations often provide the solution for acquiring the necessary GIS data. This report discusses one of the most significant collaborations the University of Saskatchewan GIS Library has been involved with, the Saskatchewan Geospatial Imagery Collaborative (SGIC). The report will outline the collaboration, its goals and outcomes, as well as provide examples of how various members of the collaboration are utilizing the data. Lessons learned through this collaboration are also discussed, which can aid other libraries interested in collaborating to purchase special types of data.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".