Physical and mechanical properties affecting the suitability of black ash wood for W8banaki basketry
Bibliographic record
Abstract
Black ash ( Fraxinus nigra Marsh.) is an important species for the W8banaki Nation, which uses its wood for traditional basketry. This study aimed to identify the wood properties required for black ash splints used in basketry. Eleven logs were selected and pounded into 26 longitudinal groups of annual wood layers, which were then transformed into splints. A quality class (high, medium, or low) was assigned by W8banakiak knowledge carriers to each group of rings. We measured wood density, ring width, modulus of elasticity, and modulus of rupture on samples located at the same radial position in wood bolts collected adjacently to the logs used for pounding. To investigate which wood properties were best related to the assigned wood quality class, we applied a generalized linear mixed model. Our model revealed a significant effect of ring width and average ring density on the probability to obtain a given wood quality class. Narrow- to medium-sized rings and relatively dense wood offered the best quality for basketry practice. Based on our results, further research on the effects of growth conditions that favour the production of high-quality black ash wood could be conducted to ultimately propose silvicultural treatments and management strategies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".