EFFECT OF FUEL PRICES ON COST-EFFECTIVENESS OF HEATING SYSTEMS FOR BROILER POULTRY BARNS
Bibliographic record
Abstract
Recent advances in extraction have increased the supply of natural gas and increased the relative price difference between it and alternative fuels. However, natural gas is not available in many rural areas forcing poultry producers unable to access natural gas to use more expensive fuels. This paper determines the least cost appliance system and fuel source for heating a broiler chicken barn in Ontario, Canada. The empirical model estimates the amount of heat required for poultry production, selects appropriate heating appliances and fuel types, and calculates the final present value of costs over a 20-year period. Appliances examined include box heaters, radiant tube heaters and biomass boilers; fuels examined include natural gas, propane, heating oil and biomass. Natural gas is the least cost fuel for both box heaters and radiant tube heaters assuming there is an existing connection to a gas pipeline. However, natural gas heating systems become the most expensive approach if the poultry operator has to pay for a pipeline connection to the gas source. With no direct connection for natural gas, biomass boilers are the most cost efficient heating system, followed closely by radiant tube heaters fuelled by propane. Heating oil is the most expensive fuel examined and its costs are nearly double that for comparable box heaters and radiant tube heaters using propane.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".