Bibliographic record
Abstract
Agriculture can potentially contribute to Canada meeting its commitment to reduce net greenhouse gas (GHG) emissions under the Kyoto protocol. A representative crop - livestock feeding farm on the Canadian prairies is used to estimate the cost of net GHG abatement, taking into account CO2 equivalent emissions and carbon sequestration. Optimal cropping systems use direct seeding and continuous cropping, production systems that have lower net GHG emissions. Livestock feeding uses rations with high energy concentration (grain based) because they are more profitable and also produce less methane per animal than forage based diets. Reducing tillage is the least costly means of lowering net emissions ($20/t CO2 eq.), followed by reducing cattle feeding ($32/t CO2 eq.). If emission reductions are high or cattle numbers can not be reduced, cropping is altered to use very little nitrogen fertilizer ($272-567/t CO2 eq.), and cattle feeding is switched to a higher forage diet (up to $1500/t CO2 eq.). The high forage diet has lower emissions per capacity animal, but only because one-half the number of animals can be finished with the same facility capacity. A regional analyses of aggregate emissions will need to incorporate the heterogeneity of farms and soil carbon levels that exist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".