Bibliographic record
Abstract
Anaerobic digestion (AD) of manure from confined feeding operations (CFOs) to produce biogas and in turn electric power in farm or feedlot based units as well as centralized plants is evaluated for two settings in Alberta, Canada: a mixed farming area (Red Deer County), and an area of concentrated beef cattle feedlots (Lethbridge County). Centralized plants need to transport manure to the plant and digestate back to the source CFO, an added cost relative to farm or feedlot based plants, but gain from the economy of scale in plant capital and operating cost. Based on a county survey of manure sources, a centralized plant drawing manure from 63 sources in the mixed farming area, at a manure yield of 34 dry tonne year-1 km-2, could produce 5.9 net MW of power at a cost of $240 MWh-1 (in 2005 U.S. dollars). No individual CFO in the mixed farming area, including a 7,500 head beef cattle feedlot, could produce power at a lower cost with a farm or feedlot based unit. Based on manure yields from an Alberta-based 1 MW demonstration AD plant, a centralized plant drawing manure from 560,000 beef cattle in Lethbridge County, at a manure yield of 280 dry tonne year-1 km-2, could produce power at a cost of $150 MWh-1. In Lethbridge County, an individual feedlot larger than 20,000 head of beef cattle could produce power at a lower cost than the centralized plant. Commercial processes to recover concentrated nutrients and a dischargeable water stream from digestate are not available. However, the theoretical impact of digestate processing was analyzed based on a capital cost of 2/3 of the AD plant itself. Digestate processing shifts the balance in favor of centralized processing, and a feedlot would need to be larger than 250,000 head to produce power at a lower cost than a centralized plant. Power from biogas has a high cost relative to current power prices and to the cost of power from other large-scale renewable sources. Power from biogas would need to be justified by other factors than energy value alone, such as phosphate recovery, pathogen reduction, or odor control.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".