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Record W4401649471 · doi:10.22533/at.ed.9734102412081

INTERNATIONAL COMPETITIVENESS OF THE EXPORT BUSINESS OF CONCENTRATED APPLE JUICE

2024· article· en· W4401649471 on OpenAlexaboutno aff
Marco Schwartz, Matías Gomez

Bibliographic record

VenueJournal of Agricultural Sciences Research (2764-0973) · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsRevealed comparative advantageIndex (typography)Openness to experienceBusinessCompetition (biology)International tradeChinaMarket shareComparative advantageAgricultural economicsEconomicsGeographyMarketing

Abstract

fetched live from OpenAlex

The size of the international market for apple juice concentrate (JCM) is USD 2000 million.The main actors are China, Poland and Turkey, which explain 55% of the participation, while Chile has a share of 3.7%.Due to the high competition of this economic activity, it is relevant to analyze the competitiveness of this agroindustry.The objective of this work was to determine the competitiveness of the Chilean JCM export business.A total of 8 competitiveness indicators were determined for 32 countries, with greater appreciation in exports, in the period 2015-2019: Revealed comparative advantage index, Tradability, Degree of export openness, Degree of import penetration, Market specialization index, Distance between the supplier and the buyer, Lafay Index and Market Insertion Matrix.These were assigned a score to prepare a ranking, in which Poland, Chile and Serbia stand out as the most competitive countries.Potential markets (Canada, India, Netherlands, Russia and Spain) were identified through an international demand matrix.Chile is a competitive country in the export of JCM, however, trade agreements do not provide greater competitiveness, because other suppliers sell at lower prices and/or are located at a shorter distance from the destination.The fact that the Chilean supply is not burdened with tariffs is not enough.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.319
Teacher spread0.260 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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