Anchorage of coniferous trees in relation to species, soil type, and rooting depth
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Bibliographic record
Abstract
A database was constructed of tree-anchorage measurements from almost 2000 trees from 12 conifer species that were mechanically overturned on 34 sites in the United Kingdom between 1960 and 2000. Anchorage was compared among species, soil groups (freely-draining mineral, gleyed mineral, peaty mineral, and deep peat) and root depth classes (shallow, <40 cm; medium, 40–80 cm; and deep, >80 cm) using regressions of critical turning moment against stem mass. Sitka spruce (Picea sitchensis (Bong.) Carr.) was used as a benchmark because it formed the largest part of the database and was the only species with all soil-group and depth-class combinations. Anchorage of Sitka spruce was strongest on peat and poorest on gleyed mineral soils. Deep rooting increased critical turning moments by 10%–15% compared with trees of equivalent mass with shallower roots. Significantly better anchorage than Sitka spruce was found for grand fir (Abies grandis (Dougl. ex D. Don) Lindl.), with various rooting depths on freely draining and gleyed mineral soils and for Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) on medium-depth mineral soil. Lodgepole pine (Pinus contorta Dougl. ex Loud.) had poorer anchorage than Sitka spruce over a range of soil groups and root depth classes. Norway spruce (Picea abies (L.) Karst.) on shallow gleyed mineral soil, and Corsican pine (Pinus nigra subsp. laricio (Poir.) Maire) on medium depth mineral soil, also had poorer anchorage. Other combinations had similar anchorage to the equivalent Sitka spruce. These results are discussed with respect to the development of forest wind-risk models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it