Vigilancia tecnológica e inteligencia competitiva para identificar oportunidades y amenazas a la producción y exportación de productos peruanos de sacha inchi
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
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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