Energy and greenhouse gas intensity of corn (Zea mays L.) production in Ontario: A regional assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. Jayasundara, S., Wagner-Riddle, C., Dias, G. and Kariyapperuma, K. A. 2014. Energy and greenhouse gas intensity of corn (Zea mays L.) production in Ontario: A regional assessment. Can. J. Soil Sci. 94: 77-95. Analysis of the environmental impact of corn (Zea mays L.) uses, such as biofuels and bioproducts, requires cradle to farm-gate life-cycle analysis of energy use and net greenhouse gas (GHG) emissions associated with corn production. Previous analyses have been based on case studies. Here we present an analysis based on census data for Ontario at the county level that was performed for three scenarios: (1) corn cultivated only for grain; (2) corn cultivated for grain and 30% stover harvest; and (3) corn cultivated for grain and cob harvest. Energy intensity of corn grain at the county level varied from 1.75 to 2.17 GJ Mg-1 grain, with the largest proportions of energy being consumed for grain drying (33%), production and supply of nitrogen (N) fertilizer (30%) and diesel use for field work (17%). Overall GHG emission intensity of grain corn varied from 243 to 353 kg CO2eq Mg-1 grain, of which 72% were associated with N inputs [34% soil nitrous oxide (N2O) from synthetic fertilizer N (SFN), 13% from SFN production and 10% from applied manure N]. Energy intensity of corn stover and cobs was 0.96 and 0.36 GJ Mg-1 dry matter, respectively, with the largest proportion of energy associated with production and supply of replacement nutrients. Intensity of GHG emission was 79 and 31 kg CO2 eq Mg-1 dry matter for stover and for cobs, respectively. Counties with higher corn yields at lower N application rates and reduced tillage tended to produce corn with lower energy and GHG intensity per Mg grain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 it