Inteligencia comercial y su influencia en la exportación de la fruta caqui al mercado canadiense
Bibliographic record
Abstract
ABSTRACT \nThe proposed study is called: "Commercial intelligence and its influence on the export of \npersimmon fruit to the Canadian market", and establish as its main objective Identify how \ncommercial intelligence influences the export of persimmon fruit to the Canadian consumer \nmarket. An applied research of quantitative and qualitative descriptive type, extracted by \nexternal primary and secondary sources in order to collect existing information to analyze \nthe variables, and provide the necessary data to make sound decisions about market research, \nopportunities and investment risk. The population taken into account is the statistical data of \nthe periods 2014 to 2018 and as a technique the collection of historical statistical data from \nTRADEMAP, SIICEX, SUNAT, etc. will be used. to understand the current situation and \nvisualize the future trends of the market under study. The main conclusion recognizes that \nthe appropriate application of commercial intelligence methods and concepts are an \ninfluential factor in the export of exotic fresh fruits, such as persimmon, within the Canadian \nmarket and that it depends a lot on the strategies and analysis applied the success or failure \nof an international marketing proposal. \nKeywords: Commercial intelligence, export, persimmon, Canadian market.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".