Realização do Processo de Editoração Cartográfica Utilizando Aplicativos Livres de Geoprocessamento
Bibliographic record
Abstract
The production of cartographic mapping has undergone many transformations over the years, from extensive use of analog instruments to digital media. Currently, much of this process is accomplished with the help of computers, requiring a large investment in computer systems. In the early 90's came a movement called Free Software, which aims to reduce dependence on technology to large multinational organizations that dominate many segments of information technology. Thus, several programs were developed for general use and replacing it satisfactory so-called proprietary software. Lately, it has also been developed Software for the area of geoprocessing. The map publishing process is essential in the production of a map and requires mastery of several techniques, one of the most complex tasks of cartography. This study presents a new way to accomplish this important task, seeking to harness the latest advances in mapping using free software and automating parts of the process to facilitate its implementation. Another important issue explored in this work is the use of technologies related to web services, in view of the importance acquired by the Internet in disseminating all sorts of information. The Internet has gained greater importance in the dissemination of spatial data from the time when several countries have invested in Spatial Data Infrastructures (SDI's) such as Canada, with GeoConnections and the European countries with the Inspire.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.054 |
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".