Impact of land classification on potential warm season grass biomass production in Ontario, Canada
Bibliographic record
Abstract
Kludze, H., Deen, B., Weersink, A., van Acker, R., Janovicek, K. and De Laporte, A. 2013. Impact of land classification on potential warm season grass biomass production in Ontario, Canada. Can. J. Plant Sci. 93: 249-260. This paper examines the land base of southern Ontario to determine the capability of land classes for growing two warm-season grasses, switchgrass (Panicum virgatum) and miscanthus (Miscanthus spp.), and discusses implications of a provincial biomass industry strictly based on biomass grown on marginal lands. The development of a biomass energy industry is a priority for many regional governments in Canada as a means to reduce fossil fuel use and improve environmental quality. Biomass productivity of the two crops was determined by assuming percentages of arable land area by quality that could be allocated to them: biomass productivity on “prime lands” was assumed to be higher than those of “marginal lands”. Our analysis indicates that Ontario has an adequate land base for producing miscanthus and/or switchgrass biomass to meet and surpass diverse competitive uses without significantly affecting food crop supply. Locations of marginal lands are scattered in the province and the feasibility of establishing a provincial biomass industry strictly based on biomass grown on these lands may not be economically sound or practical. A relatively small percentage of prime lands is required to achieve substantial biomass production with lower costs of production, and perhaps greater environmental benefit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".