TREND: Foreign Agricultural Service, United States Department of Agriculture. World Agricultural Production, Supply, and Distribution: Agricultural Commodities | Country: Canada | Commodity: Sugar, Centrifugal | Attribute: Exports, 1960 - 2019. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 008-007-001
Bibliographic record
Abstract
datasets.shared.infosheet.CitationMgr@e81 Dataset: Provides data on 94 agricultural commodities, categorized at a high level as Coffee, Cotton, Dairy, Field Crops, Fruits and Vegetables, Grains, Juice, Livestock, Oilseeds, Poultry, Sugar, Tobacco, and Tree Nuts. Key attributes (190) of production are presented, ranging from exports, imports, total production, consumption, and more. Included in this dataset are official current and historical United States Department of Agriculture (USDA) data on the production, supply, and distribution of agricultural commodities for the United States and key producing and consuming countries. The data are collected by the USDA Foreign Agricultural Service, which maintains a global agricultural market intelligence and commodity reporting service to provide US farmers and traders with information on world agricultural production and trade for use in adjusting to changes in world demand for US agricultural products. Data are reported as marketing year averages. (Marketing year represents a one-year period, beginning at the start of the new harvest for a commodity and extending to the same time in the following year.) http://www.fas.usda.gov/psdonline/psdDownload.aspx Category: Agriculture and Food, International Relations and Trade Subject: Agricultural Imports, Agricultural Exports, Agricultural Production, Agricultural Commodities, Food Consumption, Agricultural Products Source: United States Department of Agriculture The United States Department of Agriculture (USDA) was created by the Agricultural Act of May 15, 1862, signed into law by President Abraham Lincoln. Key activities include: expanding markets for agricultural products and support international economic development; further developing alternative markets for agricultural products and activities; providing financing needed to help expand job opportunities and improve housing, utilities, and infrastructure in rural America; enhancing food safety by taking steps to reduce the prevalence of food-borne hazards; improving nutrition and health by providing food assistance and nutrition education and promotion; and managing and protecting America's public and private lands working cooperatively with other levels of government and the private sector. Major data statistical programs of USDA include the Economic Research Service, the Foreign Agricultural Service, the National Agricultural Statistics Service, and the World Agricultural Outlook Board. https://www.usda.gov/
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.080 |
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".