Species-specific combination and calibration between area-based and tree-based diameter distributions using airborne laser scanning
Bibliographic record
Abstract
The planning of wood procurement requires reliable information about the species-specific timber assortments on which the economic value of a production forest depends. The timber assortments refer to the stem volumes of the sawlog and pulpwood fractions, specified in terms of both timber quality and allowable log dimensions, e.g., the stem diameter at breast height (dbh). We propose here an airborne laser scanning based calibration framework for generating species-specific dbh distributions that combines the area-based approach (ABA) and individual-tree detection (ITD), two established and independent approaches for retrieving forest attributes from airborne laser scanning data. Both ABA- and ITD-derived dbh distributions were generated nonparametrically for pine, spruce, coniferous, deciduous, and all species and assessed with respect to the plot-level species-specific total stem volume (m 3 ·ha –1 ) and approximations of volume of timber assortments. Although after calibration, the total volume of all species and the volume approximations of coniferous sawlog and spruce pulpwood decreased in accuracy by 4%–7%, the calibration improved the accuracy of the other 12 species-specific estimates by 2%–17%, testifying to the general effectiveness of the proposed calibration framework.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".