A Simple Meta-Model for Assessing the Contribution of Liquid Fossil Fuel for On-Farm Fieldwork to Agricultural Greenhouse Gases in Canada
Bibliographic record
Abstract
ABSTRACT Although fuel used for farm fieldwork contributes less than 10% of on-farm greenhouse gas (GHG) emissions, a range of commodities, scales of farming and regional constraints, and implement choices make farm machinery fuel-related GHG emissions highly variable. Many of the same decisions that affect farm fuel use also affect soil carbon levels, as well as other sustainable agriculture issues such as amount of pesticides used under reduced tillage. Because the conversational format of the Farm Fieldwork and Fossil Fuel Energy and Emissions model (F4E2) makes its program unsuitable as a subroutine for other models, a meta-model of the F4E2 was developed. Meta-modeling, the calibration of a simple model against a complex model, has proven successful in several previous situations where the complexity or input requirements of an original model limited its application. The Meta-model of Emissions from Fossil Fuel for Farm Fieldwork (MEF4) was designed as a sub-model to a GHG-Calculator being developed for estimating all GHG emissions from Canadian agriculture. Inputs to MEF4 comply with the more holistic farm management questions of the GHG-Calculator. The basis of MEF4 is a set of regional, operation-specific fossil fuel-based GHG emissions generated from the F4E2 model. Some versatility was incorporated into MEF4 by deriving adjustment factors from ratios of emissions from specific operations simulated by F4E2. For example, fuel use in harvesting operations was adjusted to the average yields from a range of crops. Reduced tillage adjustments required subtracting the GHG emissions attributed to the prior tillage operation (plowing and/or disking) from the total for spring fieldwork. Soil draft resistance for the remaining spring operations, disking or seeding, were then adjusted by 40% and 20%, respectively, while rolling resistance was un-affected. To verify MEF4, ten farm scenarios generated from F4E2 were compared to the meta-model estimates. The average ratio of corresponding MEF4 and F4E2 estimates was 0.98 with a standard deviation of 0.03, indicating that this set of ratios was not significantly different than a one-to-one line. While MEF4 is an adequate meta-model of F4E2, it also reduces the complexity of farm machinery management to a level suitable for integration with the GHG-Calculator and other processes or models to assess total fuel energy balance and GHG emissions of Canadian agriculture. MEF4 illustrates how a simple model can be incorporated with other models to assess interactions among a range of sustainable agriculture issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".