Bibliographic record
Abstract
Wood is a common material for the manufacture of many products. Submerged wood, in particular, is used in niche markets, such as the creation of musical instruments. An initial study performed on submerged wood from Ootsa Lake, British Columbia, provided results that showed that the wood was not suitable for musical instruments. This thesis re-examined the submerged wood samples. After allowing the wood to age unabated in a laboratory setting, the wood was retested under the hypothesis that the physical acoustic characteristics would improve. It was shown, however, that the acoustic properties became less adequate after being left to sit. The adsorption properties of the submerged wood were examined to show that the submerged wood had a larger accessible area of wood than that of control wood samples. This implied a lower amount of crystalline area within the submerged wood. From the combined adsorption and acoustic data for the submerged wood, relationships between the moisture content and speed of sound were created and combined with previous research to create a proposed model to describe how the speed of sound varies with temperature, moisture content and the moisture content corresponding to complete hydration of sorption sites within the wood. --P. ii.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".