Bioenergy opportunities from agriculture. Retrieved May 21, 2009 from http://www.reap-canada.com/online_library/ghg_offsets_policy/19Bioenergy%20opportunities%20from%20Agriculture%20_1998_.pdf
Bibliographic record
Abstract
Solar energy collection in the form of agricultural biomass can play an important role mitigating climate change. Crop residues and dedicated biomass energy crops can be used to meet a diversity of energy needs including electricity, space heating and liquid transportation fuels. Perennial grasses appear to be the most promising dedicated feedstocks for energy production in regions with limited crop residues. Perennial grasses are well adapted to marginal soils, intercept sunlight throughout the growing season, have low maintenance requirements and a low cost of production. As a greenhouse gas offset strategy they can help reduce CO2 buildup in the atmosphere by sequestering carbon in the soil and by producing biofuels with closed loop carbon cycles. In northern regions with cool summers, reed canary grass, a C3 species, appears to be the most promising dedicated agricultural biofuel feedstock. In areas with warmer summer temperatures, switchgrass, a warm season (C4) prairie grass, is the lowest cost dedicated agricultural feedstock, with delivery supply costs of $38-$51 per oven dry tonne (odt) or $2.17-$2.91per gigajoule (GJ) of energy. Warm season (C4) grasses have several agronomic advantages and energy conversion advantages over C3 grasses. Sustainably grown agricultural biofuels combined with energy efficient end use technologies can play an important role to facilitate the implementation of a sustainable energy economy. Recent breakthroughs in cellulosic ethanol and gasifier pellet stove technology currently allow for extended opportunities in agriculturally derived biofuels as a means of meeting greenhouse gas reduction targets.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.172 | 0.065 |
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".