Stem and root system response of a Norway spruce tree (Picea abies L.) under static loading
Bibliographic record
Abstract
Abstract The mechanical response of a well-instrumented Norway spruce (Picea abies L.) tree under controlled winch loading was monitored. The main aims of this study were to understand the tree-root-soil response to lateral pull loads, to examine the applicability of simple engineering principles to the tree-root-soil response and to introduce a soil component into the tree stability analysis. The stem response was recorded with tilt sensors at three different heights; two sensors at each height tracked the response in transverse directions. These data were used to derive deflection and bending moment profiles of the stem for different lateral loads. A root on the windward side and one on the leeward side were instrumented using strain gauges. The tree was winched to failure and the data were collected from the strain gauges during winching. Using the data obtained from strain gauges on the roots, strain, bending moment, shear force and deflection profiles the roots with increasing load were calculated. Using the soil reaction force-deflection profiles of the roots, equivalent spring stiffness constants were determined. An estimate of the anchorage strength and moment–rotation relationship was made using a simple characteristic curve equation normalized by the failure moment and rotation that can be modified for different soil conditions.
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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".