Representation of Generalized Map Series Using Semi-Structured Data Models
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
Large cartographic organizations worldwide produce generalized map series (GMS) in order to meet various user requirements. A GMS consists of maps of the same geographic region at different scales. Most of these maps currently are designed in a digital environment, and recently some of them have been distributed through the web. One important issue is the appropriate modeling and handling of cartographic entities composing individual maps in a GMS. Since these entities have rather complex descriptions and may be provided by various agencies, they usually do not conform to a fixed schema (i.e., they do not have a common structure). Hence, their representation in traditional data models, such as the relational or object-oriented, is not always feasible. This paper investigates the use of semi-structured data (SSD) models—an innovative approach recently developed in Information Technology for representing and handling entities in a GMS. Specifically, the Object Exchange Model (OEM), a popular database model for SSD, has been adopted to represent a GMS. How useful information can be extracted from such a representation using the LOREL query language—a popular language for SSD—is also shown.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.024 |
| 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