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Record W6920557914 · doi:10.6068/dp15e7489c4a814

Trend 1961 - 2011. Food and Agriculture Organization of the United Nations. Food and Agriculture Organization Statistics: Food Balance Sheets | Country: Canada | Item: Oats | Element: Production - 1000 tonnes, 1961-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 067-001-068.

2017· other· en· W6920557914 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureFood supplyPopulationFood processingFood systemsFood securityProduction (economics)Commodity

Abstract

fetched live from OpenAlex

Food and Agriculture Organization of the United Nations (2017). Food and Agriculture Organization Statistics: Food Balance Sheets | Country: Canada | Item: Oats | Element: Production - 1000 tonnes, 1961-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 067-001-068. Dataset: Statistics describe the pattern of a country's food supply during a specified reference period. For each food item reported, ie, each primary commodity and a number of processed commodities potentially available for human consumption, the sources of supply and its utilization are reported. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side, a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity, caloric value, and protein and fat content. 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: Food Supply, Social Development, Food Security

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.180
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.014
GPT teacher head0.219
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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