Vertical and temporal distribution of insolation in gaps in an old-growth coniferous forest
Bibliographic record
Abstract
The combination of canopy access at the Wind River Canopy Crane Research Facility, hemispherical photography, and long-term insolation data provided estimates of vertical and temporal distributions of insolation in nine canopy gaps in a 65 m tall Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) - western hemlock (Tsuga heterophylla (Raf.) Sarg.) forest. Yearly insolation (long-term data from Portland, Oreg.) exhibited a sigmoidal pattern with height, with a bright zone (>4200 MJ/m2) above 50 m, a transition zone from 45 to 30 m (2000 MJ/m2), and less rapid decrease from 30 to below 10 m (600 MJ/m2). Intergap variation peaked between 20 and 40 m. Interannual variation of yearly insolation (CV = SD/mean) was about 5% throughout the canopy. Seasonality of insolation was driven by solar angle and cloudiness. Diffuse insolation was 50% of annual above-canopy flux, increasing to nearly 70% at 1.5 m, and diffuse proportion was greater in winter and spring. Hourly simulations under clear and cloudy conditions provided an appropriate time scale for modeling photosynthesis. Estimated leaf area index peaked at 30-35 and 5-10 m but was underestimated (3.7 vs. 9.1 m2/m2from direct measurements) because of foliage clumping. The methods documented highly variable distributions of insolation driven by forest structure, cloudiness, and seasonal changes in solar angle.
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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) | 0.000 | 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".