Root anchorage and stem strength of black spruce (Picea mariana) trees in regular and irregular stands
Bibliographic record
Abstract
A tree winching experiment was conducted, simulating wind action and resulting damage, in order to assess mechanical resistance of black spruce (Picea mariana (Mill.) B.S.P.) trees in stands of regular and irregular structures. The critical bending moment (Mc) of winched trees was determined and the relationship between Mc and tree and stand characteristics was investigated through statistical analyses. Stem mass (SW), average spacing between trees (S) and height:diameter ratio (H/d.b.h.) were initially selected by a screening procedure to predict Mc. Potential differences between stand structures were tested in mixed models using different subsets of the data. Included fixed effects varied between the different models. SW was the most useful and significant variable in all models and H/d.b.h. was significant only when snapped trees were included in the analysis. When decayed samples were excluded, resistance to uprooting was higher in irregular stands. Decay seemed to play an important role in irregular stands and should be investigated further. Since no difference was observed in the relationship between stem mass and critical turning moment between distant sites, relationships should be applicable across wide regions. This study provides some of the basic relationships required to model windthrow risk in irregular stands. However, the effects of stand structure on wind load for individual trees will also need to be considered.
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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) | 0.001 | 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".