Utilizing accurately positioned harvester data: modelling forest volume with airborne laser scanning
Bibliographic record
Abstract
Modern cut-to-length harvesters are recording information about each harvested tree, and with accurate positioning, this information can be used as field reference data, replacing manually measured reference data. In the present study, models developed from accurately positioned harvester data were compared with a reference model. A set of ∼55 000 accurately positioned trees was used as the basis for a division into 792 reference plots of 400 m 2 each. A set of manually measured field plots was used for validation. Regression models were developed based on the relationship between airborne laser scanning data and the reference plot volumes. Separate models were developed for two strata: medium and high site productivity. Several modelling methods were compared, including nonparametric models; at the plot level, predictions for the validation dataset yielded RMSEs of 32%–60% for the medium productivity stratum and 19%–22% for the high productivity stratum. A reference model was fitted to the manually measured validation data in each stratum, and RMSEs of 45% and 25% were obtained for the medium and high productivity strata, respectively. The results show that the models based on the harvester data yield prediction errors at the same level as the reference model.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".