Sistema de Informação Geográfica (SIG) e base de dados geoespaciais do projeto Geodegrade
Bibliographic record
Abstract
Geographic Information Systems (GISs) came into light when Canadian researchers started solving problems using different kinds of data accessed by a system of large-scale transport models. GISs have basically four components. Input data are the first component; acquired and generated data management is the second; edition (manipulation) and analysis functions, which will determine what is to be generated by the GIS, are the third one; and the fourth and last component are the output data, which will be differentiated by their quality, accuracy and easiness of use, and which, similarly to the input data, may be reproduced as maps, tables and graphs. The project 'Development of Geotechnologies for the Identification and Monitoring of Pasture Degradation Levels GeoDegrade' aims to develop geotechnologies to identify and monitor the degradation level of pastures in the Amazônia, Cerrado and Mata Atlântica biomes. In the project, the GIS will be used to standardize, organize and integrate different types of data obtained and generated by the project in a single database: 1. primary data obtained at the field; 2. previously surveyed geospatial information, digital or printed; and 3. images from satellites. In this context, this work has the aim of demonstrating the use of the GIS as a tool for the management and organization of the information platforms generated by the GeoDegrade project.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".