Leaf area index of an old-growth Douglas-fir forest estimated from direct structural measurements in the canopy
Bibliographic record
Abstract
Leaf area index (LAI) in old-growth Douglas-fir (Pseudotsuga menziesii var menziesii (Mirb.) Franco) forests exceeds that of any other forest ecosystem by some estimates; however, LAI determinations in coniferous forests have generally been indirect, involving extrapolations of patterns observed in younger stands. Aided by a 75-m construction crane for canopy access, we used a vertical line-intercept method to estimate LAI for a [Formula: see text]450-year-old Douglas-fir - western hemlock (Tsuga heterophylla (Raf.) Sarg.) forest in southwestern Washington state. LAI was calculated as the product of foliage contact frequency and an "extinction coefficient" accounting for foliage angular distribution, geometry, and the ratio of "interceptable" to total leaf area. LAI estimates were 9.3 ± 2.1 (estimate ± 95% confidence interval), 8.5 ± 2.2, and 8.2 ± 1.8 in 1997, 1998, and 1999, respectively, or 8.6 ± 1.1 pooled across years. Understory vegetation, including foliage of woody stems <5 cm diameter, represented 20% of this total. Sample points in which Douglas-fir was dominant had a higher total LAI than points dominated by western hemlock, including a higher LAI of understory vegetation. Our results do not support the contention that old-growth Douglas-fir - western hemlock forests maintain an appreciably higher LAI than do other forest ecosystems. Moreover, LAI in very old stands may decline as western hemlock replaces Douglas-fir through the course of succession.
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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 | 0.000 | 0.000 |
| 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".