Método para classificação tipológica da floresta urbana visando o planejamento e a gestão das cidades
Bibliographic record
Abstract
Abstract. The need of the handling of the vegetations in the urban nuclei has been one of the sharpest challenges, tends in view the accumulation of environmental problems in the last times. In the United States and Canada, many efforts are being invested in the researches, so much in level local as national, where techniques and instruments are developed to quantify structures with approach multidisciplinar that can serve as aid to the planning. There are few data relatively on quality typology related to the inherent function to the soil use and the urban morphology. This work seeks to tie the quality of the space with the vegetation and to present a schematic form to organize the urban vegetations, suggesting a classification typology of an urban forest. The proposed method approaches a road that integrates the urban morphology into the vegetable use of the space, capable to contemplate the vegetable structuring and the dynamics of the structuring of the expansion of cities. The result shows categories and typologies of the vegetations in a schematic way, suggesting the use of System of Geographical Information (SIG), for a practice of environmental exploration.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".