Tree species, crown cover, and age as determinants of the vertical\n distribution of airborne LiDAR returns
Bibliographic record
Abstract
Light detection and ranging (LiDAR) provides information on the vertical\nstructure of forest stands enabling detailed and extensive ecosystem study. The\nvertical structure is often summarized by scalar features and data-reduction\ntechniques that limit the interpretation of results. Instead, we quantified the\ninfluence of three variables, species, crown cover, and age, on the vertical\ndistribution of airborne LiDAR returns from forest stands. We studied 5,428\nregular, even-aged stands in Quebec (Canada) with five dominant species: balsam\nfir (Abies balsamea (L.) Mill.), paper birch (Betula papyrifera Marsh), black\nspruce (Picea mariana (Mill.) BSP), white spruce (Picea glauca Moench) and\naspen (Populus tremuloides Michx.). We modeled the vertical distribution\nagainst the three variables using a functional general linear model and a novel\nnonparametric graphical test of significance. Results indicate that LiDAR\nreturns from aspen stands had the most uniform vertical distribution. Balsam\nfir and white birch distributions were similar and centered at around 50% of\nthe stand height, and black spruce and white spruce distributions were skewed\nto below 30% of stand height (p<0.001). Increased crown cover concentrated the\ndistributions around 50% of stand height. Increasing age gradually shifted the\ndistributions higher in the stand for stands younger than 70-years, before\nplateauing and slowly declining at 90-120 years. Results suggest that the\nvertical distributions of LiDAR returns depend on the three variables studied.\n
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".