The New Age of Topographic Data – Management and Access
Bibliographic record
Abstract
The Centre for Topographic Information in Sherbrooke (CTIS) has reviewed the way it produces (or updates), manages, and disseminates vector topographic data (NTDB data and others). First of all, the clientele was broken down into segments in order to better respond to specific needs (specialized customers, privileged customers, the general public, and crisis managers). We defined new vector data specifications taking into account customer requirements to a greater degree (update cycle, accuracy, accessibility, change management). International standards (ISO/OGC) were used to better describe data characteristics and to make use of concepts familiar to the geomatics community. Landsat 7 and GPS technology have been used to produce the initial version of this new geospatial data. The NTDB data (150 Gb) and the upcoming data will be stored in a database management system (Oracle8I Spatial). The spatial data will be available in several GIS formats through Web sites using e-commerce technology. This paper focuses on the characteristics of these new Geospatial Database (GDB) specifications, with particular emphasis on spatial relationship expressed by the Dimensionally Extended Nine-Intersection Model (DE-9IM), update management (historical data), and data access.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.021 | 0.047 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".