Diatoms Biotechnology: Various Industrial Applications for a Greener Tomorrow
Bibliographic record
Abstract
The benefits of the complex microscopic and industrially important group of microalgae such as diatoms is not hidden and have lately surprised the scientific community with their industrial potential. The ability to survive in harsh conditions and the presence of different pore structures and defined cell walls have made diatoms ideal cell machinery to produce a variety of industrial products. The prospect of using a diatom cell for industrial application has increased significantly in synch with the advances in microscopy, metabarcoding, analytical and genetic tools. Furthermore, it is well noted that the approach of industry and academia to the use of genetic tools has changed significantly, resulting in a well-defined characterization of various molecular components of diatoms. It is possible to conduct the primary culturing, harvesting, and further downstream processing of diatom culture in a cost-effective manner. Diatoms hold all the qualities to become the alternative raw material for pharmaceutical, nanotechnology, and energy sources leading to a sustainable economy. In this review, an attempt has been made to gather important progress in the different industrial applications of diatoms such as biotechnology, biomedical, nanotechnology, and environmental technologies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".