Trend 1961 - 2013. Food and Agriculture Organization of the United Nations. Food and Agriculture Organization Statistics: Production - Indices | Country: Canada | Item: Livestock (PIN) | Element: Gross Production Index Number (2004-2006 = 100) - Int. $, 1961-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 067-001-014.
Bibliographic record
Abstract
Food and Agriculture Organization of the United Nations (2017). Food and Agriculture Organization Statistics: Production - Indices | Country: Canada | Item: Livestock (PIN) | Element: Gross Production Index Number (2004-2006 = 100) - Int. $, 1961-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 067-001-014. Dataset: Presents indices of agricultural production that show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2004-2006. The indices are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional, and world levels are calculated by the Laspeyres formula. Production quantities of each commodity are weighted by 2004-2006 average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period. Because the indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting them (once in production data and once with the crops or livestock produced from them). The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. The category of food production includes commodities that are considered edible and that contain nutrients. Accordingly, coffee and tea are excluded along with inedible commodities because, although edible, they have practically no nutritive value. Indices for meat production are computed based on data for production from indigenous animals, which takes account of the meat equivalent of exported live animals but excludes the meat equivalent of imported live animals. Statistics are presented in "international commodity prices," which are used in order to avoid the use of exchange rates for obtaining continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These "international dollars" are derived using a Geary-Khamis formula for the agricultural sector, which assigns a single price to each commodity. The time-series and cross-sectional data provided here are from the FAOSTAT database of the Food and Agriculture Organization of the United Nations. Statistics include measures related to the food supply; forestry; agricultural production, prices, and investment; and trade and use of resources, such as fertilizers, land, and pesticides. As available, data are provided for approximately 245 countries and 35 regional areas from 1961 through the present. The data are typically supplied by governments to FAO Statistics through national publications and FAO questionnaires. Official data have sometimes been supplemented with data from unofficial sources and from other national or international agencies or organizations. In particular, for the European Union member countries, with the exception of Spain, data obtained from EUROSTAT have been used. Category: Agriculture and Food, International Relations and Trade Source: Food and Agriculture Organization of the United Nations Established in 1945 as a specialized agency of the United Nations, the Food and Agricultural Organization’s mandate is to raise levels of nutrition, improve agricultural productivity, better the lives of rural populations, and contribute to the growth of the world economy. Staff experts in seven FAO departments serve as a knowledge network to collect, analyze, and disseminate data, sharing policy expertise with member countries and implementing projects and programs throughout the world aimed at achieving rural development and hunger alleviation goals. The Statistics Division of the Food and Agricultural Organization collates and disseminates food and agricultural statistics globally. http://www.fao.org/ Subject: Agricultural Production, Agricultural Commodities, Food Production, Agricultural Products, Food Products
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.092 |
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".