Combined effect of nutrient and flashing light frequency for a biochemical composition shift in <i>Nannochloropsis gaditana</i> grown in a quasi‐isoactinic reactor
Bibliographic record
Abstract
Abstract Artificial lighting may be an interesting opportunity for the cultivation of microalgae as an alternative to natural sunlight. In particular, light emitting diodes (LEDs) can be employed to tailor the lighting to the microalgal culture in a controlled mode in order to create flashing light. In order to establish the effect of the flashing frequency on growth and biochemical composition of a model microalga, a quasi‐isoactinic reactor, in which the light distribution is almost homogeneous, was set up. In this work, it was employed for the cultivation of the heterokont Nannochloropsis gaditana in two growth media with limiting and nonlimiting nutrients. The combined effect of nutrient concentration and flashing frequency on the growth, lipid content, fatty acid content, and pigment content was assessed for the first time. The results indicate that both nutrient concentration and flashing frequency influence the above‐mentioned parameters. In particular, under flashing light conditions, an increase of lipid content and a decrease of polyunsaturated fatty acids (PUFAs) and chlorophyll are observed when nutrients are deficient, while the opposite effects are shown when nutrients are abundant.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".