Revisiting the Concept of Geospatial Data Interoperability within the Scope of Human Communication Processes
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
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 .
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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