Anais do 24ª Simpósio Brasileiro de GeoInformática (GEOINFO)
Bibliographic record
Abstract
This volume of Proceedings comprises the papers presented at the XXIV Brazilian Symposium on Geoinformatics, GEOINFO 2023, held from December 4 to 6, 2023, at the National Institute for Space Research (INPE) in Sao Jos´e dos Campos, Brazil. The GEOINFO conference series, inaugurated in 1999, continues its mission of convening researchers and students to explore innovative applications in geographic information science and related areas. The Federal University of ABC (UFABC) and INPE were responsible for organizing this edition. The Program Committee accepted 65 papers. The program included 26 oral presentations divided into 6 technical sessions and 39 posters across 3 sessions. The event welcomed over 100 participants from more than 30 national and international institutions. GEOINFO has a rich tradition of attracting world-renowned researchers to engage productively with our community, fostering intriguing exchanges and discussions at the forefront of the field. This year, we were honored to have special keynote presentations by Dr. Antonio Paez from the School of Earth, Environment & Society at McMaster University, Canada, and Dr. Claudia Bauzer Medeiros from the University of Campinas (UNICAMP). Dr. Paez presentation delved into the trajectory of time-geography and its perspectives in the era of Big Data and Mobility Analytics. Meanwhile, Dr. Claudia Bauzer Medeiros discussed the new frontiers shaped by geospatial data collection as well as the challenges of collecting and preserving data for open science practices. Additionally, this edition featured a mini-course on Digital Image Processing for rapid disaster response, conducted by Dr. Laercio Namikawa from INPE.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.017 |
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".