Demography and biomass change in monodominant and mixed old-growth forest of the Congo
Bibliographic record
Abstract
Abstract: Mbau forest covers much of the Congo, and shifts in its composition could have a large impact on the African tropics. The Ituri forest in east Congo is near a boundary between the monodominant mbau type and non-mbau mixed forest, and two 20-ha censuses of trees ≥ 1 cm diameter were carried out over 12 y to monitor forest change. Based on published diameter allometry, mbau forest had 535 Mg ha−1 biomass above ground and gained 1.1 Mg ha−1 y−1. Mixed forest had 399 Mg ha−1 and gained 3 Mg ha−1 y−1. The mbau tree (Gilbertiodendron dewevrei) increased its share of biomass from 4.1% to 4.4% in mixed forest; other common species also increased. Sapling density declined at both sites, likely because increased biomass meant shadier understorey, but the mbau tree increased in sapling density, suggesting it will become more important in the future. Tree mortality and growth rates were low relative to other tropical forests, especially in the mbau plots. Shifting toward G. dewevrei would represent a large gain in carbon in the mixed forest, but mbau is presently more important as a high-carbon stock: biomass lost during forest harvest could not recuperate for centuries due to slow community dynamics.
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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".