Universal Geospatial Ontology for the Semantic Interoperability of Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ontologies have been used to support the interoperability of geospatial data by overcoming semantic problems related to semantic heterogeneities and to differences in data usage contexts. Ideally, to solve semantic heterogeneities, the data models involved in the interoperability process could be enriched, and the relationships between their elements could be defined based on a universal geospatial ontology. However, such ontology would encounter difficulties in achieving an efficient interoperability. This chapter aims to argue that universal ontology-based interoperability remains vulnerable to the risks of uncertain meaning of geospatial data that may go unnoticed during the interoperability process. The chapter discusses these risks and proposes a systematic approach to better support users dealing with them. The proposed approach identifies the risks, assesses their severity, and helps users to make decisions about them.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it