Is wood characteristics mapping an opportunity to optimize the value chain in Northwestern Ontario? a case study considering eastern larch (Larix laricina (Du Roi) K. Koch) grown in the Thunder Bay District / by Scott Miller.
Bibliographic record
Abstract
"Wood characteristic mapping was considered as a means for optimizing the value chain of northwestern Ontario tree species. A literature review was completed which investigated the relationship of wood morphology to wood characteristics and end use as related to potential opportunities for northwestern Ontario. It was found that there was insufficient study on the area of interest to make any definitive conclusions; save that research is needed. The literature did, however, provide a general understanding on issues being assessed. Based on the findings of the literature review, a case study on mapping wood characteristics of eastern larch (Larix laricina (Du Roi) K. Koch) grown in the Thunder Bay district was completed. It was found that the greatest variability displayed by eastern larch wood grown in Thunder Bay district was between sites and radial position within trees. In all cases of statistical analysis, variance between sites was significant. Radial variability was significant for all the selected wood properties tested except for MOE perpendicular to the grain. Longitudinal or axial variability was significant in all the selected wood properties tested except for wood density.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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".