From Unloved Woods to Diserable Renewable Biofuels
Bibliographic record
Abstract
Increasing global demand for energy, a push by governments and industry to reduce greenhouse gases (GHG), and a desire to increase energy independence are driving the demand for renewable alternatives to fossil fuels. As a source of renewable carbon that can be used in the existing energy infrastructure, woody biomass is an attractive feedstock for the production of bioenergy (meant here to include biomassbased energy carriers in solid, gaseous and liquid forms) in the form of heat, power and liquid transportation biofuels.[...] Under the current forest harvest regimes in Ontario, Quebec, and Atlantic Canada, a substantial volume of dead, damaged, and low-grade trees go unutilized despite being part of the annual allowable cut (the government-dictated maximum harvest volume) or available harvest area. Forest operators leave these materials on site because they do not meet quality requirements for lumber and/or pulp production (Barrette et al. 2015). These types of low-grade trees are often too dry or too rotten, which restricts their suitability for the conventional forest industry (Barrette et al. 2012). For example, they may have been affected by fungi, cankers, cambial necrosis, trunk fissures or foliage loss.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.009 |
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".