Synthetic biology advances towards a bio-based society in the era of artificial intelligence
Bibliographic record
Abstract
Synthetic biology is a rapidly emerging field with broad underlying applications in health, industry, agriculture , or environment, enabling sustainable solutions for unmet needs of modern society. With the very recent addition of artificial intelligence (AI) approaches, this field is now growing at a rate that can help reach the envisioned goals of bio-based society within the next few decades. Integrating AI with plant-based technologies, such as protein engineering, phytochemicals production, plant system engineering, or microbiome engineering, potentially disruptive applications have already been reported. These include enzymatic synthesis of new-to-nature molecules, bioelectricity generation, or biomass applications as construction material. Thus, in the not-so-distant future, synthetic biologists will help attain the overarching goal of a sustainable yet efficient production system for every aspect of society. • Artificial intelligence tools are emerging as valuable gears in synthetic biology. • Plant-based products and technologies are key solutions for a bio-based society. • Plant protein and cell engineering enable designer systems with novel functions. • Microbiome engineering opens new avenues for sustainable agriculture.
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.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".