SPATIAL DATA INFRASTRUCTURE. BENEFITS AND STRATEGY
Bibliographic record
Abstract
Nowadays, the geoscience data have become widely available in different organizations, which play a very important role in decisions-making at different levels (social, economic, political…). However, these organizations use standards, technologies and policies that differ from one to another. Therefore, this information is increasingly being distributed widely and become divorced from their original context or had remained limited to a small scale. Hence, the need for a spatial data infrastructure (SDI) becomes a necessity in order to facilitate the creation, sharing, and access to geospatial data, thus the exchange of knowledge between them, using a minimum set of standard practices, protocols, and specifications. The establishment of a spatial data infrastructure is to create conditions to ensure free access of public authorities, local authorities, organizations and citizens to spatial data. This paper presents a preliminary study of implementation of a spatial data infrastructure. It introduced the SDI developments in USA, Canada and Europe and summarized the relevant benefits.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".