Revisiting the Concept of Geospatial Data Interoperability within the Scope of Human Communication Processes
Bibliographic record
Abstract
Geospatial data interoperability has been the target of major efforts by standardization bodies (e.g. OGC, ISO/TC 211) and the research community since the beginning of the 1990s. It is seen as a solution for sharing and integrating geospatial data, more specifically to solve the syntactic, schematic, and semantic as well as the spatial and temporal heterogeneities between various representations of real–world phenomena. A few models have been proposed to automatically overcome heterogeneity of geospatial data and, as a result, increase the interoperability of geospatial data. However, the addition of a conceptual framework of geospatial data interoperability would contribute to understanding geospatial data interoperability, the appreciation of where existing contributions specifically apply, and would foster new contributions. In this paper, we revisit the concept of geospatial data interoperability within the broader scope of human communication and cognition. Human communication appears to be a rich framework since humans interoperate more easily than computers do. Accordingly, we present a conceptual framework of geospatial data interoperability that is broader in scope than existing frameworks and supported by several practical examples. An ontology of geospatial data interoperability is also introduced in order to refine the description of the conceptual framework. In such a communication–based framework, the notions of concept, context, proximity, and ontology appear to be fundamental elements. These elements constitute a new approach to geosemantic proximity.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.010 | 0.026 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".