Vigilancia tecnológica e inteligencia competitiva para identificar oportunidades y amenazas a la producción y exportación de productos peruanos de sacha inchi
Bibliographic record
Abstract
A technological watch is applied to the exportable supply of Peruvian sacha inchi \nproducts in order to identify opportunities that Peru can take advantage of to improve \nits product offer and the detection of threats that may affect its current and favorable \npositioning in international markets. To perform the technological surveillance, the \nmethod proposed by Fernández et al. (2009) was used. This is based on the processes \nof selective dissemination of information used by professionals in information science \nin academic or specialized libraries. The results revealed threats to the production of \nPeruvian sacha inchi as the low impact of Peruvian scientific production in generating \na competitive advantage for the development of new export products, especially \nagainst China and other countries in the region such as Brazil and Colombia. It also \nidentified the limited use of intellectual protection tools, such as patents and registered \ntrademarks that, rather, are used by other countries such as Canada, the United States, \nChina, and other Asian countries to ensure the commercialization of their innovative \nproducts in the most important markets of the world.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".