Impact of Conservation Tillage on Landscape & Ecological Services: Challenges and Opportunities
Bibliographic record
Abstract
Environment Canada’s (EC) mandates to protect and strengthen Canada’s environment and economy can be achieved more effectively by working cooperatively with individuals and agencies in agricultural and other sectors. To help in achieving these overarching environmental and economic goals, EC has been working with the agricultural sector, governments and landowners to help identify, evaluate and implement agricultural practices, programs and policies to maintain or enhance Canada’s air, water and biological resources and Canada’s economic competitiveness. These environmental and economic goals also are critical components of EC’s Science Plan (2006) and its results planning structure. In this presentation, I will review briefly the main environmental challenges and opportunities concerning the continued use and development of no-till operations. Although there is a range of conservation seeding practices, here I focus on no-till, a practice that involves a single pass of equipment over a field with direct seeding into the previous year’s crop. This executive summary is organized on the basis of key EC mandates noted above, with special emphasis on natural upland and wetland habitats of the Canadian prairies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".