A fine-scale model for area-based predictions of tree-size-related attributes derived from LiDAR canopy heights
Bibliographic record
Abstract
We propose a conceptual (generic) allometric (power function) relationship between tree-size-related forest inventory attributes (e.g. biomass, volume, basal area, quadratic mean diameter, Lorey's height) and canopy height (CH) as estimated from first-return airborne light detection and ranging (LiDAR) pulses. A data-driven estimation of the parameters in the power function is complicated, so we recommend an alternative approximation obtained via a linearisation step. Only two predictors appear in the approximation: the mean CH and the variance of CHs within the spatial domain supported by field data. The proposed model eliminates an otherwise complex search for the best predictors amongst a large number of candidate LiDAR metrics. It also facilitates model comparisons and interpretation. Fit statistics estimated for volume, basal area, quadratic mean diameter and Lorey's height – using three separate datasets from Norway – were compelling.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".