Does steep terrain influence tree stability? A field investigation
Bibliographic record
Abstract
The anchorage of 40-year-old Sitka spruce (Picea sitchensis (Bong.) Carr.) trees grown in a plantation on a steep (ca. 30°) slope was compared with that of trees grown on an adjacent horizontal area. There was similar gleyed mineral soil on the sloping and horizontal areas. Trees were mechanically overturned using a winch, and anchorage was quantified by measuring load, stem angle, and tree dimensions. Trees on the slope were overturned upslope, downslope, or across-slope. Critical turning moments were calculated around the tree base and the actual hinge point. Critical wind speeds required to uproot or snap trees in this stand were modelled to compare the vulnerability of trees to upslope and downslope winds. No overall difference in anchorage was found between trees grown on the horizontal and sloping parts of the site. However, for trees on the slope, those pulled upslope showed significantly more resistance to overturning for a given stem mass than those pulled downslope. Critical turning moments calculated at the hinge point were smaller than those calculated at the stem base, but differences were small and had no effect on the comparison between treatments. Critical wind speeds for uprooting were estimated to be 28 m·s 1 for an upslope wind and 24 m·s 1 for a downslope wind on this site. The implications of these results are discussed in relation to windthrow-risk modelling and forest soil conservation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".