Geovisualização analítica: desenvolvimento de um protótipo de um sistema analítico de informações para a gestão da coleta seletiva de resíduos urbanos recicláveis.
Bibliographic record
Abstract
Domestic waste illegally disposed constitute a serious problem, especially in large cities, causing clogging of drains and drainage with subsequent flooding, dirt and transmission of diseases such as leptospirosis and dengue as well as being a hindrance to traffic and would entail city hall costs.Moreover, recycle this material is a source of revenue and a generator of jobs.The Analytical Geovisualization can be of great help in the analysis of this complex problem and the decision-making.With this motivation, this paper seeks to provide an overview of the state of the art as the concepts and research in Geovisualization (GVis) and Analytical Processing (OLAP and SOLAP).It also presents processes, methodologies and technologies that were used in developing a prototype of an analytical system applied to the area of selective collection information management of recyclable solid waste in urban environments.This prototype, effectively deployed, combined navigation and query to retrieve information through spatial and alphanumeric selections.Through examples it was shown that this type of solution helps managers in operating procedures, analysis and decision making.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".