Adaptive co-management of urban forests: monitoring reforestation programs in Mexico City
Bibliographic record
Abstract
Aiming to maintain or increase the indispensable socio-ecological benefits provided by urban forests, cities of the world have adequate urban forestry to take advantage of new technologies and governmental arrangements. Cooperation among different actors has become a trend to address urban forests' most pressing management issues, such as reforestation monitoring and the creation of tree inventories. This management approach has been conceptualized as adaptive comanagement (ACM) in European and North American cities. Intending to advance the academic efforts to understand ACM, this article presents a spatial and statistical analysis of the distribution of trees monitored in Mexico City. The analysis indicated that the number of urban trees monitored is very low and inequitably distributed in the city; poor areas of the city are not only underserved of green public spaces and trees but have also been neglected in terms of monitoring reforestation programs. The implementation of ACM for environmental management of the urban forest, using the participatory tool of Naturalista, developed by (in Spanish, Comisin Nacional para el Conocimiento y Uso de la Biodiversidad, CONABIO). The tool demonstrated to have much potential in the operationalization of inclusive reforestation programs, particularly in monitoring urban trees recently planted. The implementation of ACM and citizens' science programs are discussed and recommended as a promising urban environmental management approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".