Monitoring secondary tropical forests using space-borne data: Implications for Central America
Bibliographic record
Abstract
Tropical secondary forests, which play an important role in carbon sequestration, may be monitored using space-borne sensors. Secondary forest biomass or age estimation from space-borne data may be used to quantify the carbon sink these forests represent. At current capabilities, roughly three successional stages up to 15 years of age may be identified from Landsat TM data. Using synthetic aperture radar, reliable biomass estimates may be made up to approximately 60 tons/ha. The potential for overcoming these limitations is reviewed, including the synergy of radar and optical imagery and the unprecedented spatial and spectral resolutions of new sensors. Most of the available literature to date is from the Amazon; in this paper, applicability to Central America is considered, which has a much more heterogeneous landscape and the dynamics of secondary growth have a special significance in the framework of conservation biology and carbon sequestration. We conclude that critical issues in this region will be topographical correction and stratification according to ecological and site quality variables.
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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 | 0.001 |
| 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 | 0.001 | 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".