Analysis of five simulated straw harvest scenarios
Bibliographic record
Abstract
Almost 36 million tonnes (t) of cereal grains are harvested annually on more than 16 million hectares (ha) in Canada. The net straw production varies year by year depending upon weather patterns, crop fertility, soil conservation measures, harvest method, and plant variety. The net yield of straw, after discounting for soil conservation, averages approximately 2.5 dry (d)t ha-1. Efficient equipment is needed to collect and package the material as a feedstock for industrial applications. This paper investigates the costs, energy input, and emissions from power equipment used for harvesting straw. Five scenarios were investigated: (1) large square bales, (2) round bales, (3) large compacted stacks (loafs), (4) dried chops, and (5) wet chops. The baled or loafed biomass is stacked next to the farm. Dry chop is collected in a large pile and wet chop is ensiled. The baling and stacking cost was $21.47 dt-1 (dry tonne), with little difference between round and large square baling. Loafing was the cheapest option at $17.08 dt-1. Dry chop and piling was $23.90 dt-1 and wet chop followed by ensiling was $59.75 dt-1. A significant portion of the wet chop cost was in ensiling. Energy input and emissions were proportional to the costsmore » for each system, except for loafing, which required more energy input than the baling systems. As a fraction of the energy content of biomass (roughly 16 GJ dt-1), the energy input ranged from 1.2% for baling to 3.2% for ensiling. Emissions from the power equipment ranged from 20.3 kg CO2e dt-1 to more than 40 kg CO2e dt-1. A sensitivity analysis on the effect of yield on collection costs showed that a 33% increase in yield reduced the cost by 20%. Similarly a sensitivity analysis on weather conditions showed that a 10oC cooler climate extended the harvest period by 5-10 days whereas a 10oC warmer climate shortened the harvest period by 2-3 days.« less
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".