Investigation of phyco-remediation of road salt run-off with marine microalgae<i>Nannochloropsis gaditana</i>
Bibliographic record
Abstract
Phyco-remediation is an environmental-friendly method, which involves the application of beneficial microalgae to treat wastewater-containing pollutants for a diverse range of conditions. Several industrial processes generate hyper saline wastewater, which is a significant challenge for conventional wastewater treatment, and the disposal of saline waters also has a negative impact on the environment. Road salt run-off is one such saline wastewater stream not currently treated and one that contributes significantly to negatively impacting receiving bodies of water. In this study, Nannochloropsis microalgae were able to assimilate >95% of the nitrates within 8 days in road salt concentrations ranging from 2.6% to 4.4% under phototrophic cultivation mode. Biomass yields of 1-2 g/l of culture were obtained with the maximum lipid of 22% (g/g) biomass in the road salt media. The crude road salt media provided all the essential micronutrients needed for algal cultivation. The fatty acid composition analysis of the obtained lipid composed of C16 and C18 over 45% of FAME are suitable for biofuel. This study has established that the use of road salt containing nitrate and phosphate nutrients will support the growth of marine micro algae for remediation of a waste water system that are the concern at winter-prevalent regions.
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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) | 0.000 | 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 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".