Using MFA and density values of White Spruce to develop a prediction model for wood static bending strength
Bibliographic record
Abstract
This study concentrates on establishing a prediction model for the MOE of tree species White Spruce by analysing the MFA and density values of about 159 samples. Clear specimens measuring 25.4 mm X 25.4 mm X 406.4 mm (1”X1”X16”) were tested for bending strength and stiffness during summer of 2008. The data was then used to calculate the stiffness or MOE (Modulus of Elasticity) and strength or MOR (Modulus of Rupture). Further analysis of the samples was carried out to obtain density and MFA (Micro Fibril Angle). A correlation was then drawn up between the MOE, Density and MFA values. Samples were scanned for density from pith to bark by an X-ray densitometer. MFA was calculated using a Bruker D8 Discover X-Ray Diffractometer. All the measurements were carried out in metric units. The correlation between the MOE and Density was found to be 0.22 and between MOE and MFA was 0.34. The combined correlation of MOE with Density and MFA was 0.43. MOE and MOR were found to have a correlation value of 0.66.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".