Five years of research in energy plantation in southern Quebec (Canada)
Bibliographic record
Abstract
Since 1989, short rotation and intensive culture (SRIC) techniques have been experimented to grow various fast growing species of trees and shrubs for energy plantation or environmental purposes. The objectives were to evaluate the biomass productivity with respect to (1) different weed control methods during the establishment phase; (2) drainage conditions and soil quality of the plantation site; and (3) frequency of coppicing. Native or introduced species of willows and various species of shrubs, such as honeysuckle and cornel, were grown in an experimental design in the nursery of the Montreal Botanical Garden on former agricultural land. Productivity, in tons of dry material per hectare, was evaluated and compared by harvesting shoot and branch samples at the end of each growing season. Weed control is essential to the establishment of trees in SRIC. When weed repression was applied during the two first growing seasons, biomass productivity was 4 to 5 times greater than the biomass produced on the control plot of the well drained site. With good weed control, willows can yield more than 20 tons of dry material on well drained site and near 15 tons on a poorly drained site, only two years after plantation. The growth potential of shrub species is also interesting. Some of them were able to produce up to 10 tons of dry biomass per hectare per year, which is appreciable considering that such species can be used on marginal lands and for the fixation of river banks. Frequency of coppicing also influences productivity. For willows, we determined that three-year rotation cycle allowed the highest biomass productivity. Shrubs should be coppiced each year to obtain the best results. Fast growing species and SRIC techniques are not only a good way of producing wood and alcohol for energy but they also represent a way of rapidly colonizing degraded or marginal sites and of fixing river banks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".