Predicting wood quantity and quality attributes of balsam fir and black spruce using airborne laser scanner data
Bibliographic record
Abstract
The objective of this study was to determine whether a suite of wood quantity and quality attributes of balsam fir and black spruce forests could be predicted using airborne laser scanner data. In situ estimates of stand structure and wood fibre attributes were derived from measurements at sample plots covering a wide range of forest conditions of insular Newfoundland. Models developed to predict field estimates explained 52–90 per cent of the variation in structure attributes, including mean and quadratic mean diameter at breast height, average and dominant height, stem density, basal area, total and merchantable volume and above-ground total biomass. Cross-validated root mean square errors were <24 per cent of mean values, with the exception of stem density, for which errors were 27–32 per cent. Models of fibre attributes explained 18–53 per cent of the variation in fibre length, wood density, radial diameter, coarseness, microfibril angle, modulus of elasticity, wall thickness and specific surface with cross-validated root mean square errors of <14 per cent of mean values. Similar results were achieved for fibre attribute models derived using geographic, climate and vegetation variables. The results demonstrate potential for inventory of quantity and quality attributes over a large region of boreal forests in Newfoundland, Canada.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".