An investigation of biodiesel production from microalgae found in Mauritian waters
Bibliographic record
Abstract
The aim of this study was to assess the lipid content and the subsequent potential of different microalgae present in the Mauritian marine water to produce biodiesel. The share of micro-phytoplankton species in the water column was determined. The cyanobacterial mats and endosymbiotic dinoflagellates were characterised morphologically and genetically using RFLP. The samples were quantified gravimetrically and analysed using 1H &13C NMR spectroscopy. Total micro-phytoplankton count amounted to 6.59±1.27x105 cells L-1which was dominated by diatoms (95.2%), followed by dinoflagellates (2.9%) and cyanobacteria (1.9%). The cyanobacterial mats were identified as Leptolyngbya sp. and Nodularia harveyana, and the RFLP characterised the endosymbiotic dinoflagellates as the Symbiodinium clade C. The highest amount of lipid was recorded in the Symbiodinium clade C (38.39±6.58%). 1H and 13C NMR analyses indicated the presence of acyl glycerols. An attempt to synthesise biodiesel by alkaline trans-esterification reaction was also performed and the presence of biodiesel was detected using the Fourier Transform Infrared Spectroscopy. The Infrared analysis yielded peaks at around 1738cm-1 and 1200cm-1 characteristic of the carbonyl and ether groups respectively, indicating the presence of biodiesel.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".