Analysis of stand density effects on the stem form of Norway spruce trees and volume miscalculation by traditional form factor equations using terrestrial laser scanning (TLS)
Bibliographic record
Abstract
Tree and stand volume estimates are relevant for forest inventories, forest sales, and carbon stock evaluations. Forest practice commonly uses generalized stem-wood volume functions; however, such generalized approaches neglect the stem form in detail. Hence, trees of a given species with the same diameter at breast height (d 1.3 ) and height (h) are always assumed to have the same form factor and thus the same volume. This case study focused on stem form variation of Norway spruce (Picea abies (L.) Karst.) due to competition effects. Using terrestrial laser scanning (TLS), we measured the stem shape of 868 trees from a long-term spacing and thinning experiment in Germany. The plots covered a broad density range. We analysed the effect of competition and compared the TLS-determined stem volume estimates with those determined conventionally. TLS-based volume estimations showed that the lower the competition was, the lower the tree volume was with a given d 1.3 and h. Commonly used functions underestimated the volume stock overall by 4.2%, disregarding any levels. At plot level, underestimation varied from 0.7% to 7.0%. At tree level, the volume was under- and over-estimated by −10% to +10%, respectively. The more precise the examination was, the more suitable the application of TLS was for enhancing volume estimation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".