Effect of size and number of calibration plots on the estimation of stem diameter distributions using airborne laser scanning
Bibliographic record
Abstract
Stem diameter distribution is a crucial forest inventory variable in operational forest management. Compared to ground based forest mensuration (e.g., diameter at breast height (DBH)), airborne laser scanning (ALS) offers a cost effective alternative for modelling forest inventory variables. The objective of this study is to determine the impact of the size and number of sample plots on modelling diameter distributions in an unevenaged tolerant hardwood forest using discrete return ALS data. With the size of the sample plots ranging from 0.04 to 0.25 ha, DBH distributions were divided into six structural classes, estimated by two categories of non-parametric methods: k-nearest neighbor (k-NN) imputation and the random forest (RF). Sensitivity analysis demonstrated that the size of sample plots has a stronger impact on model performance than the number of plots. In addition, RF was found to be the most accurate model, regardless of the size and number of sample plots.
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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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".