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Record W7026563390

Análisis de la ventana de oportunidad del mercado Canadiense y su influencia en las exportaciones Peruanas de Sacha Inchi

2014· dissertation· en· W7026563390 on OpenAlexaboutno aff

Bibliographic record

Venuerenati · 2014
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOrder (exchange)Heading (navigation)International marketWindow of opportunityContent analysisMarket research
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT \nThe following study called ―Analysis of the Canadian’s market window of \nopportunity and its influence on the Peruvian exports of Sacha Inchi‖, regarding \nthe 2008 – 2012 period, has as its main objective to demonstrate the influence \nof the Canadian’s market window of opportunity in the Peruvian exports of \nSacha Inchi by the 2014. \nThis study is possible due to the increasing demand and interest of the \ninternational markets, especially the Canadian market, regarding the organic \nand functional healthy products such as the Sacha Inchi and its outstanding \nbenefits for the human health and the opportunities that implies for the Peruvian \nexports success of the mentioned product. In order to accomplish that, it was \nnecessary to analyze: the Canadian consumer profile, the evolution and \ntendency of the Peruvian exports to the World and Canada during the 2008 – \n2012 period, the evolution of the number of enterprises and the number of tariffs \nheading used for Peruvian exports of Sacha Inchi to the target market, and the \nCanadian Imports regarding the most representatives tariffs heading used in \nPeru for the export of the Sacha Inchi to the Canadian market, this way it was \npossible to demonstrate the success of the Sacha Inchi in the target market. \nIn conclusion, it was possible to conclude that the Canadian’s market window of \nopportunity will positively influence on the Peruvian exports of Sacha Inchi by \nincreasing them in the 2014, which is why, according to the interviewee experts; \nit should be used by the Peruvian producers and exporters in order to succeed \nin the Canadian market.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.800

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.004
GPT teacher head0.222
Teacher spread0.218 · 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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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