Outdoor performance of Chlorococcum littorale at different locations
Bibliographic record
Abstract
Our goal in the present study was to evaluate the potential for lipid production of two cell populations of the marine microalgae Chlorococcum littorale under different climate conditions. We selected, in a previous study and via fluorescence activated cell sorting (FACS), a new cell population of Chlorococcum littorale , namely S5. S5 showed a stable doubled triacylglycerol (TAG) productivity in comparison with the original population. A previously developed model was expanded to include day:night cycles and validated to predict biomass and outdoor TAG productivities at different locations. Four different locations were chosen to simulate the response of C. littorale to different day lengths and light intensities (the Netherlands, Norway, Brazil and Spain). Indoor experiments (simulated summer) were carried out with Original and S5, showing that S5 had a doubled TAG productivity under N-starvation. Finally, simulations of biomass and TAG productivities of Original and S5 at different locations were performed. At locations with lower light intensities, Norway and the Netherlands, biomass productivities were higher than at locations with higher light intensities, Brazil/Spain. Such results might be associated with light-saturation effects. TAG productivities, however, showed no effect of local light intensity. Locations at higher latitudes, Norway/Netherlands, cannot sustain phototrophic year-round production, hence, the yearly average TAG productivities were doubled in Brazil/Spain (from 1.4–1.6 to 3.0–3.2 g m − 2 d − 1 ). Likewise, C. littorale S5 was simulated with doubled TAG productivities when compared with Original, at all locations (2.5–2.7 (low light) to 4.7–5.2 g m − 2 d − 1 (high light)). The present results confirm the industrial potential of Chlorococcum littorale , both Original and S5, as a source of TAG. Furthermore, our results can be used for comparison and to estimate future production scenarios.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".