Bibliographic record
Abstract
Leaf angle distributions are important in assessing both the flexibility of a plant's response to differing daily and seasonal sun angles and also the variability in the proportion of total leaf area visible in remotely sensed images. Leaf angle distributions are presented for six conifer species, Abies grandis (Dougl. ex D. Don) Lindl., Thuja plicata Donn. ex D. Don, Tsuga heterophylla (Raf.) Sarg., Pseudotsuga menziesii (Mirb.) Franco, Picea sitchensis (Bong.) Carr. and Pinus contorta Dougl. ex Loud. var. latifolia. The leaf angles were calculated by measuring four foliar quantities, and then the distributions of leaf angles are cast in three forms: distributions of (i) the angle of the long axis of the leaf from the vertical for the range 0180°; (ii) the angle of the long axis of the leaf for the range 090°; and (iii) the angle of the plane of the leaf for the range 090°. Each of these are fit to the ellipsoidal distribution to test the hypothesis that leaf angles in conifers are sufficiently random to fit the ellipsoidal distribution. The fit was generally better for planar angles and for longitudinal angles between 0° and 90° than for longitudinal angles between 0° and 180°. The fit was also better for Tsuga heterophylla, Pseudotsuga menziesii, Picea sitchensis, and Pinus contorta than for Abies grandis and Thuja plicata. This is probably because Abies and Thuja are more shade tolerant than the other species, and so the leaves in Abies and Thuja are preferentially oriented near the horizontal and are much less random than for the other species. Comparisons of distributions on individual twigs, whole branches, entire trees, and groups of trees were done to test the hypothesis that angle distributions will depend on scale, and these comparisons indicated that the apparent randomness and goodness-of-fit increased on passing to each larger unit (twigs up to groups of trees).Key words: conifer, leaf angles, ellipsoidal distribution.
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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 | 0.001 | 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) | 0.001 | 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".