Representation of Generalized Map Series Using Semi-Structured Data Models
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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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".