Predicting and calibrating tree attributes by means of airborne laser scanning and field measurements
Bibliographic record
Abstract
This paper examines the calibration of airborne laser scanning based tree attribute models to separate data by applying a best linear unbiased predictor. Firstly, single Scots pine ( Pinus sylvestris L.) trees were identified from dense airborne laser scanning data. Secondly, seemingly unrelated mixed-effects models for diameter at breast height, tree height, volume, dead branch height, and crown base height were constructed using airborne laser scanning based height metrics as predictors at both the area and individual tree level. Finally, these models were calibrated to validation stands using field measurements of some of the five abovementioned tree attributes. The models were calibrated by applying the best linear unbiased predictor to predict the random stand effects for the validation stand. In a system of several models, the correlation of random effects enabled the prediction of stand effects for all models, providing the response of at least one of the models was known for one or more sample trees of the validation stand. The results showed that the accuracy of tree attribute prediction improved in most cases as the number of sample trees increased. The level of improvement was highest for volume and dead branch height. The practical importance of the results of this study lies in applications where forest stands are visited in the field, for example, before making cutting decisions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".