Microalgal phycocyanin productivity: strategies for phyco‐valorization
Bibliographic record
Abstract
Abstract Phyco‐valorization is the exploitation of microalgae and microalgal chemicals as valuable products. This paper discusses the optimization of microalgal bioreactor‐based systems for C‐phycocyanin pigment production. Various aspects contributing to system development and enhancement of phycocyanin productivity are described. A wide range of potential microalgal species have been identified for phycocyanin production; the selection of a species for mass culturing can be determined by desired bioreactor trophic mode and symbiotic relations. Research has demonstrated that species amenability to local lighting and climatic conditions, and to variations in bioreactor substrate concentrations and operational parameters, have significant impact on phycocyanin production. The simultaneous optimization of all factors contributing to system productivity may be accomplished efficiently through process modelling. A summary of established models for microalgal phycocyanin production is presented. A suggested strategy for increasing economic viability of phycocyanin production systems is their application in integrated resource recovery. Through the incorporation of phycocyanin productivity optimization principles within a phycoremediation process, the valorization of waste resources may be achieved. The simultaneous economic potential and environmentally‐forward concept of phyco‐valorization through phycocyanin production is a promising application of microalgal biotechnology awaiting further development for industrial implementation. © 2015 Society of Chemical Industry
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".