Changing Landscapes Forest: Implications for its Conservation
Bibliographic record
Abstract
AbstractThe forest landscape of southern Chile, which includes Chilean temperate forest ecosystem, has been designated as a hotspot for biodiversity conservation. However, this landscape has been transformed by land-use change. A proper knowledge about how land-use change impact this ecosystem would provide crucial information for planning conservation strategies. At the commune of Arauco – Chile, the impact of the land-use change on the spatial pattern of native forest ecosystem from 1990 to 2010 was evaluated at the landscape level. This evaluation was carried out using satellite images, landscape metrics and spatially explicit models. The loss of native forest ecosystem was of 40.7% (loss rate of 4.39% per year). Conversely, the exotic species plantations increased more than 150%. The number patches of native forest ecosystem increased more than 130%. The size distribution of patches (<100 ha) increased more than 22%. The aggregation index of native forest ecosystem decreased from 62.5 to 40.1. The loss of native forest ecosystem was caused by the expansion of exotic species plantations, which was associated with substantial changes in the spatial pattern of the forest landscape. As a strategy for conservation of the native forest ecosystem we suggest a landscape approach, using the corridor–patch–matrix model. We recommend that this strategy be complemented with land-use planning. Moreover, this strategy must be supported by a framework of environmental policies. We also recommend strengthening the existing ecological restoration programmes and developing new programmes to restore the native forest ecosystem.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".