Modelling the variation in modulus of elasticity and modulus of rupture of Scots pine round timber
Bibliographic record
Abstract
Recovery from the forest-wood chain is directly influenced by how different qualities of logs are used. Logs used directly as load-carrying members in structures are products in which MOE (modulus of elasticity when bending) and MOR (modulus of rupture) are of great importance. To achieve a more optimal bucking process in the forest, models that enable such presorting of logs for quality are desirable. In all, 533 logs from 150 trees were sampled from 10 stands with different site indices and altitudes in southern Norway. Models were developed that predicted MOE and MOR by using different stand and tree characteristics. The MOR models were compared with a model including MOE alone and a model including both MOE and different tree characteristics. The study showed that it may be a good option to combine different tree and stand characteristics with MOE. MOE and the single-tree variables diameter at breast height and mean annual ring width at breast height most reduced the residual variance when MOR was modelled. The study also showed that it is possible to perform a coarse sorting by simple stand and single-tree characteristics.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".