Census of Agriculture, 2016 [Canada]: Farm Data and Farm Operator Data [B2020 & Excel]
Bibliographic record
Abstract
Statistics Canada conducts the Census of Agriculture every five years at the same time as the Census of Population. The most recent Census of Agriculture was on May 10, 2016. The Census of Agriculture collects and disseminates a wide range of data on the agriculture industry, including the number and type of farms, farm operator characteristics, business operating arrangements, land management practices, crop areas, the number of livestock and poultry, farm capital, total operating expenses and receipts, and farm machinery and equipment. Census data provide a comprehensive picture of the agriculture industry across Canada every five years at the national, provincial and territorial levels, as well as at lower levels of geography. The Census of Agriculture is the cornerstone of Canada's Agriculture Statistics Program. Census of Agriculture data are an indispensable public and private sector tool for analysing important changes in the agriculture and food industries; developing, implementing and evaluating agricultural policies and programs such as farm income safety nets and environmental sustainability; and making production, marketing and investment decisions. Statistics Canada uses the data as benchmarks for its regular surveys on crops, livestock and farm finances between census years. This release contains all farm and farm operator data. For current Census of Agriculture data refer to Statistics Canada.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.023 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.039 |
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".