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Record W4249179647 · doi:10.1787/c587f51e-el

OECD-FAO Agricultural Outlook 2017-2026 (Summary in Greek)

2017· other· en· W4249179647 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOECD agricultural outlook .../OECD-FAO agricultural outlook · 2017
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Development and Policies
Canadian institutionsnot available
FundersAgriculture and Agri-Food CanadaChinese Academy of Agricultural SciencesEconomic Research ServiceEuropean CommissionU.S. Environmental Protection Agency
KeywordsAgricultureRegional scienceAgricultural economicsGeographyEconomicsArchaeology

Abstract

fetched live from OpenAlex

The food and agriculture sector is faced with a critical global challenge: to ensure access to safe, healthy, and nutritious food for a growing world population, while at the same time using natural resources more sustainably and making an effective contribution to climate change adaptation and mitigation.Through this annual collaboration and other studies, the Organisation for Economic Co-operation and Development (OECD) and the Food and Agriculture Organization of the United Nations (FAO) are working together to provide information, analysis and advice, to help governments achieve these essential objectives.This is the 13th joint edition of the OECD-FAO Agricultural Outlook.It provides ten-year projections to 2026 for the major agricultural commodities, as well as for biofuels and fish.The pooling of market and policy information from experts in a wide range of participating countries provides a benchmark necessary for assessing the opportunities and threats to the sector.This year's Agricultural Outlook includes a special focus on Southeast Asia, a region where agriculture and fisheries have developed rapidly and undernourishment has been significantly decreased, but also a region that is on the front line of the effects of climate change and where there are rising pressures on natural resources.The Agricultural Outlook comes in the context of a wider set of international efforts to address food security and agricultural issues.Two global initiatives stand out:• The UN Sustainable Development Goals (SDGs) set ambitious targets to be achieved by 2030.Among these, the first goal is to end poverty in all its forms everywhere, while the second goalTThe Agricultural Outlook, 2017-2026, is a collaborative effort of the Organisation for Economic Co-operation and Development (OECD) and the Food and Agriculture Organization (FAO) of the United Nations.It brings together the commodity, policy and country expertise of both organisations and input from collaborating member countries to provide an annual assessment of prospects for the coming decade of national, regional and global agricultural commodity markets.The baseline projection is not a forecast about the future, but rather a plausible scenario based on specific assumptions regarding macroeconomic conditions, agriculture and trade policy settings, weather conditions, longer term productivity trends and international market developments.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.336
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0070.003
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.003
Open science0.0080.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0140.021

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 head0.017
GPT teacher head0.222
Teacher spread0.205 · 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