An overview of the Australian biomass resources and utilization technologies
Bibliographic record
Abstract
Information on Australian biomass resources including bagasse, black liquor from paper pulp production, wood waste and forestry residues, energy crops, crop wastes, food and agricultural wet waste, and municipal solid wastes is provided in the review. The characteristics of the Australian biomass are typical of those of other countries, i.e. high moisture and volatile matter, low heating value and density, and low sulfur and nitrogen content, but high Ca and Mg for woody biomass. The characteristics influence biomass utilization. Biomass is used extensively at present within Australia , primarily for domestic heating, as bagasse in the sugar industry, and for electricity generation. Biomass usage for electricity generation is increasing and is expected to reach 5.2 Mt/year by 2019-20. Exports, as wood chips, are approximately 10 Mt/year in 2000-01. Forestry residues have been estimated to be 23 Mt/year. Current technologies that utilize biomass in Australia include those for electricity and heat by direct combustion, cofiring with coal and fluidized bed combustion), for biogas generation (from landfills, and aerobic digestion, and as bio-liquids. Related to bio-liquid fuels, ethanol production from molasses and wheat is making progress. The resultant ethanol is used as a petrol extender, and a bio-diesel process is under development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".