Comparing direct and indirect methods of assessing canopy structure in a northern hardwood forest
Bibliographic record
Abstract
Several methods exist for measuring forest canopies following disturbance, and the biases and differences among them are unclear. We compared techniques for measuring the northern hardwood forest's canopy structure at the Hubbard Brook Experimental Forest, New Hampshire, following the severe ice storm of January 510, 1998. Methods included leaf area index (LAI) using LI-COR's LAI-2000, visual damage assessments based on tree branch loss, radiation estimates from hemispherical photographs, and LAI determined from litterfall. LAI-2000 measurements were not significantly related to visual damage class estimates, but were strongly correlated with radiation estimates from hemispherical photographs and average LAI values from litterfall. LAI from the LAI-2000 and litterfall differed on a point-by-point basis, but were similar at the stand scale. The LAI-2000 has the highest precision for large-scale measurements. Visual damage estimates appear adequate for assessing large-scale patterns of disturbance intensity in the northern hardwood forest, but the LAI-2000 is more accurate at quantifying canopy structure at large plot or stand scales. Hemispherical photographs may also accomplish this, but are better suited to characterizing the distribution of canopy gaps and light availability patterns over time. Litterfall provides accurate and precise measurements of small-scale LAI patterns in deciduous forests and reveals species-specific patterns.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".