Measurement of surface growth stress in Eucalyptus nitens Maiden by splitting a log along its axis
Bibliographic record
Abstract
Abstract When a log is sawn along the length, an outward bending or deflection of two half rounds arises from the release of growth stresses in the log. The relationship between this outward bending and the peripheral longitudinal growth strain in logs from a 10-year-old Eucalyptus nitens plantation was investigated. In total, 63 trees were felled and two logs (one bottom and an upper log) were cut from each felled tree. A strong association was observed between the measured longitudinal growth strain and the outward bending produced by splitting the log along its axis. A mathematical model was developed that enables calculation of the relationship between the surface axial growth stress and the opening of the split log. This involves calculation of the lateral deflection of a tapered cantilever beam of semi-circular cross-section under a known bending moment. The relationship between the opening of the slit log and the surface axial stress showed good agreement with the measured data on 126 logs. The model equation is effective in predicting the growth stress from the magnitude of the log distortion. This log sawing or splitting method is quick and reliable for assessing the surface growth stresses in small diameter trees. It could have potential in breeding programs for identifying families or clones for low-growth stresses of superior eucalypts for sawn timber production.
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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) | 0.000 | 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 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".