Too many big promises: What is holding back cyanobacterial research and applications?
Bibliographic record
Abstract
Abstract Climate change as a global crisis demands a shift from a fossil fuel-based economy to-wards sustainable solutions. Cyanobacteria are promising organisms for the truly sustainable, carbon-neutral production of various chemicals. However, so far, proof of concepts for large-scale cyanobacterial productions that produce industrial-relevant amounts of desired products are lacking. To systematically address this topic, a comprehensive overview that identifies current obstacles and solutions is missing. We conducted a quantitative survey among researchers in the cyanobacterial community. This work investigates individual experiences and challenges in the field of cyanobacteria, as well as information about specific protocols. Additionally, qualitative interviews with academic experts were conducted. Their answers were compared, and highlights were summarised. In this work, we provide for the first time a comprehensive overview of current trends and challenges as perceived by researchers in the field of cyanobacteria. Based on the results of the survey and interviews, we formulate a set of recommendations on how to improve the working conditions within the cyanobacteria research community.
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 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 | 0.066 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.028 | 0.031 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".