Current inventory and modelling of sawmill residues in Eastern Canada
Bibliographic record
Abstract
Integration within the forest industry in Canada historically developed to optimize use of the available timber resource. Sawmill residues, which were at one time considered waste, changed into a sought-after resource for panel and pulp manufactures. With the downturn in the Canadian forest industry and the 46.5% decrease in nationwide sawn lumber production between 2004 and 2009, the potential for utilizing sawmill residues to develop novel markets presents itself with added encouragement from national and global pressures towards creating a sustainable bio-based economy. Scarcity of information related to quantity and quality of local sawmill residue feedstocks leads to a lack of reliable data that can be used by entrepreneurs to develop sustainable supply chains for this resource. The development of an easily updateable sawmill database for all provinces, Manitoba eastwards, is explained in detail along with the comprehensive presentation of a sawmill residue output calculation model for sawdust, bark, chips/slabs, and shavings. These tools will help to predict current and future sawmill residue availability and aid in the most efficient uses of this resource in the bioeconomy.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".