Ministry management practices and their effect on GHG emissions in the Ontario agricultural sector - Final report
Bibliographic record
Abstract
Greenhouse gas (GHG) emissions from the agricultural and agri-food sectors result from the combined losses of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) to the atmosphere. Best management practices (BMPs) have been promoted as a means to remediate soil erosion, sedimentation and the on and offsite environmental problems associated with soil degradation. While BMPs were developed and implemented to address soil and water issues, their widespread implementation also has the potential to effect both the emission and sequestration of GHG. This study was initiated to determine the potential reduction in emission of GHG from agricultural land use through adoption of BMPs. The specific study objectives were as follows: 1) Measure and project the effect of agricultural management practices (BMPs) on GHG emissions for the period 1990 to 2012. 2) Identify and recommend technical measures to improve the efficiency and effectiveness of BMP practices to reduce GHG emissions and project these measures over the period 2000 to 2012. 3) Identify the provisions that the Ministry would need to consider in order to achieve GHG reduction scenarios of 2, 4, and 6% (based on 1990 emission estimates) with the listed BMPs or any other feasible BMP practice for the period 2008 to 2012.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".