A Structure Analysis for Ecological Management of Moist Tropical Forests
Bibliographic record
Abstract
Human interventions alter stand structure, species composition, and regeneration capacity of the forest. There is no enough information on how different management systems affect the forest structure. The main objective of this study was to analyze the differences on stand structure and species composition caused by different logging intensities. The study was conducted in a lowland evergreen moist forest of 22 000 ha in Cameroon. The forest was subdivided into three forest types with different human impacts:2-Logged,1-Logged, andUnlogged. The diameter corresponding to mean basal area of stems of2-Logged(31.8 cm, N=369 ) was almost equal to that ofUnlogged(30.1 cm, N=496 ).1-Loggedhad a lower diameter of 27.7 cm, N=530 . In the three forest types, the diameter distribution followed the inverse J-shaped curve frequently observed in natural forests. The stand basal area increased from 29.4 m2/ha in2-Logged, to 32 m2/ha in1-Logged, and to 35.3 m2/ha inUnlogged. These results indicated that logging affected natural regeneration in2-Logged. Above 60 cm dbh, the logging effect was not visible. On 103 tree species found in the sample forest, only nine were classified as harvestable commercial species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".