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Mudanças estruturais no mercado brasileiro de milho: Impactos na oferta, avaliação do armazenamento sob condições de incerteza e assimetria de transmissão de preços

2018· dissertation· pt· W2891543313 on OpenAlexaboutno aff
André Luís Ramos Sanches

Bibliographic record

Venuenot available
Typedissertation
Languagept
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyCropQuarter (Canadian coin)Agricultural scienceAgricultural economicsEconomicsForestryBiology

Abstract

fetched live from OpenAlex

This paper estimates the monthly corn supply in Brazil in terms of harvesting pace (first and second crops) and demand (domestic consumption and exportation) in the period between February 2001 and January 2018.The Brazilian corn market went through important structural changes in the first two decades of the twenty-first century, with higher supply in the second crop and growing exportations.This paper aims to identify and analyze the changes in the monthly corn supply in the Brazilian market.Results indicate that the increase in the second crop corn moved the months with higher inventories in Brazil to the third quarter of the year (July, August and September).Exportations are the highest in the following months (October, November and December), contributing for the fast supply decrease in the domestic marketavailability in Brazil is the lowest in the first quarter of the year (January, February and March).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.320

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.020
GPT teacher head0.259
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2018
Admission routes1
Has abstractyes

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