A Common Framework for Visually Reconciling Geographic Data Semantics in Geospatial Data Mapping Portals
Bibliographic record
Abstract
By leveraging emerging Web 2.0 technologies and approaches, mapping portals have the potential to expand both the capabilities and the user base of geospatial mapping. Tools that address semantic interoperability are central to achieving this vision, and the most common models for category semantics offer largely compatible capabilities to produce various semantic relationship metrics. This article therefore looks beyond any particular model for semantic representation to propose the semantic relationship matrix as a common structure for designing visual displays for semantic evaluation. The matrix provides a structure for separating semantic analysis into three distinct evaluation types, each helping users understand various aspects of concept relationships and their meaning. This approach is illustrated in the context of landcover mapping and the US Geological Survey's National Map, but the author argues that the framework also generalizes to other mapping portals that seek to integrate many data themes and sources into one geovisual interface.
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How this classification was reachedexpand
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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".