Bibliographic record
Abstract
On October 2nd, 2001 the federal environment commissioner released her annual report. In it, she offered an assessment of the environmental impact of agriculture in the Great Lakes basin and the federal government’s role. Specifically, the environment commissioner addressed: * Manure and fertilizer management * Soil erosion * Environmental impact of farm programs * Federal role in sustainable agriculture. Based on environmental audits and other analyses, the commissioner presented the following conclusions: * There is a problem with the accumulation of soil nutrients as a result of manure and chemical fertilizer applications in the Great Lakes basin * Soil erosion is a continuing problem that is not receiving adequate attention or data collection * Agriculture and Agri-food Canada (AAFC) has not adequately taken account of the environmental impact of farm programs, and farm programs can have impacts that conflict with AAFC’s stated environmental goals * AAFC has not appropriately targeted funding for the environment by region, and there is a greater need for cross-compliance in farm programs * Certain agricultural practices are unsustainable, and the framework to alter unsustainable farming practices is lacking. But are these conclusions warranted, given the mix of belief and credible evidence that typically permeates discussions of agriculture and the environment? In this special report, we provide a brief analysis of the Environment Commissioner’s report as it relates to livestock and sustainability. Specifically, we clarify a misconception in the Commissioner’s comparison between livestock waste and human waste, and discuss the sustainability of crop nutrients (loadings and uptake) in Ontario and Quebec as they relate to manure loadings and fertilizer use.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.115 | 0.009 |
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".