The 'Urbanforest' and 'Green space' Classification Model in the Spatial Arrangement of Registro-SP, Brazil
Bibliographic record
Abstract
The necessity of handling green areas relating to urban settings has become one of the vital environmental challenges in view of several accumulated environmental problems in the last few decades. In United States and Canada, many investments are being made for new techniques and instruments that are needed in environmental planning involving urban forest researches both in the local and national scale. However, there are few reports on urban forest classification connecting spatial characteristics, physical structures involving pattern and process. With the objective to classify urban green areas for integrated approach, this research have focused on urban pattern for an effective understanding of urban forest concept, green areas and urban vegetation types. The urban morphology in consistency with the urban forest Focus Chat (FC) recently designed has been applied to characterize spatial categories of urban vegetation involved in the urban arrangement of Registro-SP. Method has integrated zones, patterns and processes for technical analyses to contemplate the urban dynamics, occupation and land use. Categories of urban forest and vegetation types were derived for the urban planning and system management. The resulting pattern can be technically monitored in the use of suitable GIS (Geographical Information System) software for physical and environmental records.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".