Toward Sustainable Production of Second Generation Bioenergy Feedstocks<sup>†</sup>
Bibliographic record
Abstract
Some analysts point to continuing advances in agricultural technology and declining global population growth rates to predict a substantial surplus of agricultural land by 2050. Such surplus land could be diverted into growing biomass for renewable energy to help overcome the global challenge of climate change. Others suggest that diversion of agricultural land into bioenergy will exacerbate risk of chronic food shortage by 2050. On balance it appears that declining population growth rate, continuing technology advance, and intensifying use of existing global agricultural land could support sufficient food production as well as some bioenergy production. Competitive bioenergy requires development of second-generation (lignocellulosic) feedstocks rather than first-generation (starch, sugar, and oilseed) feedstocks. Second-generation feedstocks from woody crops have the potential to complement intensive agriculture and ameliorate its environmental impacts. Woody biomass crops may therefore have a lower effective cost than generally perceived. The potential for woody crops is indicated with an economic analysis of mallee, a woody crop being developed for low-cost biomass production in Western Australia. Mallees are short, multistemmed eucalypts grown in dispersed narrow belts, harvested on a regular short cycle, and regenerated by coppice. When integrated into the dryland agriculture of this region it has the potential to improve the economic and environmental performance of the entire system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".