Inteligencia comercial y su influencia en la exportación de la fruta caqui al mercado canadiense
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it