Pipeline vs. Truck Transport of Beef Cattle Manure
Bibliographic record
Abstract
Anaerobic digestion of manure can be conducted at a wide range of capacities. Ascapacity increases, economies of scale in capital equipment are realized but transportation costsincrease as manure must be carried longer distances to the plant site. In this study we evaluate thecost of pipelining manure from beef cattle confined feeding operations, i.e. feedlots, as an alternativeto truck transport. Pipeline transportation cost is minimized at a slurry concentration of about 12%;low concentrations require a larger pipeline, and high concentrations require higher pumping costs.Pipelining costs are highly scale dependent, while trucking costs are virtually independent of scale.Manure starts its journey to a digester on a truck; pipelining of manure is more economic thanongoing truck transport for manure from animals in excess of 95,000. Incremental net fixed costs fortrans-shipment from truck to pipeline are low for manure because equipment installed at the pipelineinlet eliminates the need for identical equipment within the digester plant; the incremental fixed cost identified in this study is the cost of a pipeline operator. A pipeline must run for a minimum distanceto recover the incremental fixed cost of trans-shipment; at 300,000 animals, the minimum economicpipeline distance is 8 km. Pipeline transport of beef cattle manure has the potential to reduce overalltransportation cost to a large centralized digester in areas such as Dodge City, Kansas orLethbridge, Alberta where very large numbers of beef cattle are in feedlots. A 50 km pipeline carryingmanure from 300,000 beef cattle has a overall transport cost of 60% of ongoing truck transport.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".