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Record W3145974299 · doi:10.22004/ag.econ.51718

Medium Term Outlook for Canadian Agriculture - International and Domestic Markets

2009· preprint· en· W3145974299 on OpenAlexaboutno aff
Pierre Charlebois, Stephan Gagne

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2009
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureMedium termBaseline (sea)ChinaAgricultural economicsTerm (time)EconomicsBusinessAgricultural sciencePolitical scienceMacroeconomicsGeographyEnvironmental science

Abstract

fetched live from OpenAlex

The purpose of this document is to describe the features of the Agriculture and Agri-Food Canada (AAFC) Medium Term Outlook for Canadian Agriculture covering the period 2009 to 2019. The outlook is an attempt to outline a plausible future of the international and domestic agri-food sectors. It serves as a benchmark for discussion and scenario analysis. The outlook makes specific assumptions and outlines their implications. Since it assumes that policies remain unchanged from existing legislation, the outlook is not a forecast of future events. In particular there are no assumptions made regarding the outcome of the Doha round of trade negotiations. It also assumes no impact from climate change and from policy to mitigate climate change nor significant animal disease outbreaks or unusual climatic conditions over the period of the outlook. The starting point of the international baseline is the so-called lower GDP slow recovery medium term scenario published in the OECD/FAO Agricultural Outlook 2009-2018. This scenario was updated in the short term using macro-economic forecasts released by the OECD in September 2009 and by the World Bank in June 2009. Exchange rates were updated to reflect the information released at the end of 2008 and in the first 6 months of 2009. The agricultural outlook was updated to reflect short term price forecasts produced and released by the U.S. Department of Agriculture (USDA) in October 2009.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.224
Teacher spread0.203 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2009
Admission routes1
Has abstractyes

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