Landscape Context for Forest Transition Success in Central Panama
Bibliographic record
Abstract
Abstract Context Secondary forests are frequently re-cleared before they can recover to pre-disturbance conditions. The identification of factors associated with passive regeneration persistence success would help planning cost-efficient forest restoration. Objectives In this paper we investigated the role that the landscape context of naturally regenerated forest patches plays for their chances to mature and persist in time in central Panama. Maturation and persistence of secondary forests are concepts often undervalued representing, however, essential requisites for an effective and long-term restoration of the ecosystem processes. Methods A unique data set of land-cover maps of central Panama was used to identify the forest patches that naturally recovered and persisted between 1990 and 2020. We developed a Random Forest Classification (RFC) calibration method to identify areas with higher likelihood of forest persistence success. Results The RFC model discriminated between areas that naturally recovered and persisted in time and areas that did not persisted with an error rate of 2%. By tuning, we obtained a precision of 0.94 (94%) in the validation test. Based on the model, we developed a prediction map of central Panama areas with higher probability (≥ 90%) of secondary forests persistence success within the next 20 years. Conclusions Tracking simple landscape and socio-economic metrics allowed for a deeper understanding of the underlying mechanisms of secondary forest persistence in central Panama. Through the development of RFC calibration method, this study maximized the reliability of the patches identified as suitable to persistence success, representing a basis for management decisions and future investigations for a successful, long-term forest-landscape restoration.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".