Canadian agricultural land-use and land management data for Kyoto reporting
Bibliographic record
Abstract
The land use and management data requirements for assessing, monitoring and reporting on the impact of agricultural production practices on the environment, especially in a country as large as Canada, are considerable. In view of the fact that environmental assessments are a relatively new phenomenon, data collection activities targeted toward these needs are not widespread. As a result, we find it necessary to acquire and integrate a variety of data sources with differing time lines, spatial scales and sampling frameworks. This paper uses our current activities with respect to Kyoto reporting as a focus to present and discuss the types of data required and the spatial analysis and integration procedures being developed to provide them. The essential data for this activity include the area of crop and land use types, land use changes since 1990, farm and land management practices and biomass production. The spatial framework selected for national analysis is the Soil Landscapes of Canada, and the primary existing data sources are the Census of Agriculture, sample-derived yield estimates and satellite-based land cover products. These are supplemented with detailed, multi-season, multi-year satellite image interpretations conducted at an ecologically and statistically stratified sample of sites across the country. The use of these data in preparing an account of greenhouse gas sources and sinks identified a number of gaps and problems, and a brief outline of future work designed to improve the data inputs is presented. Key words: Kyoto reporting, data integration, land use and management, greenhouse gases, carbon sequestration
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.029 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".