Designing innovation ecosystems for biointelligent value creation – Identification of promising technology fields and pioneer countries
Bibliographic record
Abstract
The concept of biointelligence as a convergence of life, information and engineering sciences was defined just over five years ago. Since then, several studies and scientific publications have dealt with the term and future implications of the topic. However, there has been a lack of a targeted and interdisciplinary approach to researching, developing, and designing biointelligent technologies, products, services, and business models. To implement the concept of biointelligence systematically and consistently, it is essential to examine which sub-areas of biointelligent value creation represent promising future markets and which countries and regions are potential pioneers. This article identifies hotspots in both dimensions and deduces where potential innovation ecosystems are located. To this end, comprehensive literature and database research were used to develop an indicator model to evaluate biointelligence innovation ecosystems. The result is the definition and delineation of 16 enabling technology fields, which divide the concept of biointelligent value creation into technological subject areas. Based on the data collected, our analysis results in a systematic derivation of five enabling technology fields assessed as particularly promising in connection with biointelligent value creation: smart greenhouses and smart farming, biorefineries and bioreactors , bio-computing and data storage , omics, as well as biosensors and bioactuators. Countries such as Israel, Finland, the USA , Canada, and Germany can be named pioneers in biointelligent value creation. Strong innovation ecosystems empower key stakeholders to partake in and incur the benefits of biointelligent value creation. This requires strategic and interdisciplinary partnerships between research institutions, governmental organizations and companies.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".