TREND: Statistics Canada. StatCan - Current Tables: Retail and Wholesale | Table ID: 20100069 | Table Name: Annual non-store retail survey, commodity sales | Variable 1: Pet food, supplies and accessories, at retail (Dollars) | Variable 2: N/A | Variable 3: N/A | Variable 4: N/A | Variable 5: N/A | Variable 6: N/A, 2013 - 2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 075-002-025
Bibliographic record
Abstract
Statistics Canada. StatCan - Current Tables: Retail and Wholesale | Table ID: 20100069 | Table Name: Annual non-store retail survey, commodity sales | Variable 1: Pet food, supplies and accessories, at retail (Dollars) | Variable 2: N/A | Variable 3: N/A | Variable 4: N/A | Variable 5: N/A | Variable 6: N/A, 2013 - 2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 075-002-025 Dataset: Reports statistics on retail and wholesale sales by type of establishment and product, as well as on operating profits and expenses, profit margins, and cost of goods sold. The datasets available in Data Planet represent the data tables released by Statistics Canada. These tables provide aggregate statistics on the Canadian population, and the nation’s resources, economy, society, and culture. In addition to conducting a Census every five years, approximately 350 active surveys are conducted on virtually all aspects of Canadian life. Statistics are provided for the nation as a whole, provinces, and other subnational geographies where available. For information on the statistical surveys and programs conducted by Statistics Canada, visit http://www23.statcan.gc.ca/imdb-bmdi/pub/indexth-eng.htm. The data tables replace the summary tables previously released by Statistics Canada via the CANSIM database. For FAQs on this transition, please visit https://www.statcan.gc.ca/eng/about/website-faq#a0 . https://www.statcan.gc.ca/eng/developers/wds Category: Industry, Business, and Commerce Subject: Retail Sales, Wholesale Trade, Retail Industry, Retail Stores Source: Statistics Canada Established as Canada's central statistical office by the Statistics Act of 1985, Statistics Canada is required to "collect, compile, analyse, abstract and publish statistical information relating to the commercial, industrial, financial, social, economic and general activities and conditions of the people of Canada." Its main objectives are to provide statistical information and analysis about Canada’s economic and social structure and to promote sound statistical standards and practices. http://www.statcan.gc.ca/
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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.062 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.313 | 0.238 |
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".