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Record W3134089753

Plan de negocios Fiori

2020· article· es· W3134089753 on OpenAlexaboutno aff
Rivera Becerra, Miguel Giovanni

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasingFloricultureBusinessQuality (philosophy)Consumption (sociology)Market researchBusiness planProduction (economics)MarketingDomestic marketPlan (archaeology)EconomicsGeographyInternational tradeSocial science
DOInot available

Abstract

fetched live from OpenAlex

Colombian flower growers have turned their gaze to the domestic market because only 5% of flower production in the country is marketed domestically. The remaining 95% is exported mainly to the United States (EL TIEMPO, 2019). Since its inception, floriculture was thought of as an export sector, which has boosted the quality of the products and in general of said industry, since it has faced demanding markets, and even more so, given the characteristics of the products (perishable) It has induced flower growers to develop cultivation, harvest and post-harvest systems according to the standards of the purchasing countries, such as the United States, Canada, European countries, Japan, etc. Therefore, Colombia has the experience to serve its own market. Every day the sector at the national level has been growing due to the great opportunity that presents itself. However, there are very few businesses that focus on the luxury market to supply domestic consumption and those that handle these characteristics of quality and exclusivity have very high prices that only the upper class can acquire. Taking into account the knowledge acquired in the undergraduate business administration of the Pontificia Universidad Javeriana, different diagnostic, planning and research tools were applied to determine the viability of Fiori. In the following chapters the reader will be able to find a detailed investigation and the development of a research question where very important information, recommendations and conclusions will be highlighted that will give a clear idea about the development of this business plan.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.006

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 head0.065
GPT teacher head0.205
Teacher spread0.140 · 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; both teacher heads agree on what is shown here.

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
Published2020
Admission routes1
Has abstractyes

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