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 (m3·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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".