Spatio-Temporal Analysis of Forest Fragmentation in Río Botello Catchment at Facatativá (Colombia)
Bibliographic record
Abstract
Nowadays the biodiversity loss has appeared with the search for human economic development which has reached dramatic proportions. Knowledge of biodiversity itself it is an essential factor, for finding the problems it faces and so develop appropriate control and conservation strategies. One of the main concerns in these days it is to characterize natural environments and how this have changed in recent years. The purpose of this study was to analyze the process of fragmentation of forests at the spatial and temporal level in the Río Botello catchment, Facatativá, Eastern Cordillera of Colombia, during the period 1985 to 2018. A time series of LANDSAT satellite images for 1985, 2001 and 2018 was used for this analysis, along with the CORINE LAND COVER methodology adapted for Colombia. The configuration of the identified terrestrial coverages was done with the FRAGSTATS software and the IndiFrag v2.1 application. These results show that the percentage of forests in the catchment decreased from 41% of the total area to 31% in the last 30 years, this because agricultural areas increased at an annual growth rate of 0.841 km2/year that replaced the natural forest mainly in the northeast and northwest sectors of the study area. The Eastern Cordillera of Colombia is one of the most deforested in the last 50 years. According to results it is necessary to carry out an integrated management of the catchment by different institutions to reduce the fragmentation and deforestation of natural areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".