Spatial and temporal variation of biomass in a tropical forest: results from a large census plot in Panama
Bibliographic record
Abstract
Summary We estimated the dry, living, above‐ground biomass (AGB) standing stock and its turnover in a 50‐hectare forest plot located in moist tropical forest on Barro Colorado Island, Panama. The estimates were obtained using inventory data collected every 5 years from 1985 to 2000, including measurements of all trees ≥ 1 cm diameter. Four different allometric regressions relating trunk diameter and height with AGB were compared. Based on the most consistent method, we estimated that the Barro Colorado forest holds 281 ± 20 Mg ha−1(1 Mg = 103kg) of AGB, lianas included. A third of the AGB is stored in trees larger than 70 cm in diameter. Stand‐level AGB increment (growth plus recruitment) was highest in the period 1985–90 (7.05 ± 0.32 Mg ha−1 year−1, mean ± 95% confidence limits based on samples of multiple hectares) and smallest in the period 1990–95 (5.25 ± 0.26 Mg ha−1 year−1), while AGB losses were similar during the three intervals (mean 5.43 ± 0.72 Mg ha−1year−1). This resulted in significant differences in AGB change (defined as increment minus loss) among census intervals; including branchfalls, the AGB of Barro Colorado Island increased in 1985–90 (+0.82 ± 0.84 Mg ha−1 year−1), decreased in 1990–95 (−0.69 ± 0.82 Mg ha−1 year−1), and increased again in 1995–2000 (+0.45 ± 0.70 Mg ha−1year−1). The 15‐year average was +0.20 Mg ha−1 year−1, but with a confidence interval that spanned zero (−0.68 to 0.63 Mg ha−1 year−1). Branchfalls and partial breakage of stems had a significant influence on the AGB changes. They contributed an average of 0.46 Mg ha−1 year−1to the AGB loss. About 5% of AGB increment was due to trees less than 10 cm in diameter. To test whether the AGB of tropical forests is increasing due to climate change, we propose that in each forest type, at least 10 hectares of forest be inventoried, and that measurements of the small classes (< 10 cm diameter) as well as large size classes be included. Biomass loss due to crown damage should also be estimated.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".