Laser point-quadrat sampling for estimating foliage-height profiles in broad-leaved forests
Bibliographic record
Abstract
A technique for estimating the vertical distribution of foliage area in broad-leaved forests was developed. The technique is similar to optical point-quadrat sampling, where estimates are based on heights to the lowest leaves above numerous sample locations beneath a canopy. In optical point-quadrat sampling, heights to lowest leaves are measured with a telephoto lens. Here, heights were measured using a commercially available laser range-finding instrument. The laser point-quadrat technique was tested in field studies conducted under broad-leaved forest canopies in western North Carolina and east-central Minnesota, U.S.A. Foliage-height profiles obtained by laser point-quadrat sampling were consistent with two of four published foliage-height profiles observed in 1995 at the North Carolina field locations. Total leaf area estimates obtained by laser point quadrats were not significantly correlated with values of leaf area index estimated by recent litter fall analyses at the North Carolina and Minnesota field locations. Although further evaluation and refinement of the technique is needed, laser point-quadrat sampling shows promise as a means of obtaining foliage-height profiles at a significantly reduced effort and with greater accuracy than methods commonly in use today.
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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 | 0.002 |
| 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 | 0.000 |
| 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 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".