Monitoring the Diameter growth of irregular trunk trees in the Celtis forest in northern Republic of Congo
Bibliographic record
Abstract
Information on the growth of tropical trees is essential for the management of tropical forests. However, tree diameter measurements taken over irregularities result in negative diameter growth due to trunk shrinkage over time. The use of the close-range photogrammetric approach to harmonise diameter measurements of irregular trunk trees is likely to improve the diameter growth of these trees. This study uses close-range photogrammetric point cloud data and conventional measurements collected on 72 irregular trunk trees at Loundoungou to examine the diameter growth of irregular trunk trees in the Celtis forest in northern Republic of Congo. Significant differences were observed in the diameter above the irregularities and at 1.30 m from the ground between 2014 and 2021, suggesting the evolution of the trunk from 2014 to 2021. The relative change in diameter above the irregularities was 4 times greater than the diameter at 1.30 m above ground. Variations in trunk diameter growth were observed within each diameter type, with the rate of diameter growth above irregularities higher for the data set and for larger diameter trees. Diameter growth models using diameter at 1.30 m above ground were best (lowest AIC and BIC), suggesting that diameter at 1.30 m above ground is, therefore, the most appropriate predictor for irregular trunk trees. The results of this study highlighted the ability of the close-range photogrammetric approach to detect diameter growth at 1.30 m above ground, which is important for improving forest carbon balance estimates and decision-making in tropical forest management. published by the International Journal of Biosciences (IJB)
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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 | 0.000 |
| 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) | 0.000 | 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".