Bibliographic record
Abstract
Utilizing the surplus production capacity of the agricultural sector to produce biofuels could play an important role in improving Canada’s energy security and meeting its international obligation to reduce greenhouse gas emissions. Energy farming with perennial grasses and fast growing trees appears to be an ideal means of collecting large amounts of solar energy from low quality and marginal farmland to process into biofuels. The objective of this project was to develop a comprehensive data set on the performance and agronomic limitations of several bioenergy feedstocks in Eastern Canada. Part One of this report examines the productivity of switchgrass and short rotation forestry (SRF) willow in a side by side paired comparison study established to assess their economic and agronomic viability in eastern Canada. The study was repeated on two sites in Ste-Anne-de-Bellevue, Quebec. Seven year average annual yields were 11.5 oven dry tonnes (ODT) ha-1 for Cave-in-Rock switchgrass and 11.0 ODT ha-1 for SRF willow. Switchgrass was easier to establish, less prone to weed and insect infestation and easier to manage than SRF willow. Fall harvesting switchgrass is risky due to high plant moisture levels (41-51%) and poor conditions for field drying. Germplasm with earlier fall maturity and good lodging resistance was identified that
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.609 | 0.500 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".