Bibliographic record
Abstract
New standards for geospatial data representation are emerging. For example, the ISO (the International Organization for Standardization) geospatial metadata draft standard defines a new object-oriented representation schema. Existing collections of geospatial data and metadata need tools to transform them to the new standard. This research investigated how mapping from existing geospatial metadata standards can be formally specified and implemented using XML. In addition, we investigated how large collections of geospatial data can be indexed to permit fast search for data queries combining spatial ranges with keywords and date range. To test out research ideas, we implemented the translation of Canadian NTDB (National Topographic Database) metadata files into the FGDC (Federal Geographic Data Committee) CSDGM (Content Standard for Digital Geospatial Metadata), which can then be translated into XML files. A tool for transforming FGDC CSDGM XML metadata files to ISO XML metadata files was designed and implemented in two ways: XSLT (eXtensible Style Language Transformations) and a Java program written for this research. A formal grammar for ISO geospatial metadata standard was proposed as a way of generating the XML DTD (Document Type Definition). Search engines for searching ISO XML metadata files on the Web by geospatial coordinates, dates and strings were developed by using a GSDindex (geospatial data index based on R-tree and AVL trees) approach and a relational(Oracle 8) database approach. Experiments comparing the two search engines on a testbed containing 6979 geospatial metadata files showed that, on average over a set of seven search experiments, the GSDindex approach was 2.5 times faster than the Oracle database approach. -iv- Acknowledgments I would like to...
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.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".