Morphological characterization, growth appraisal, and probing biofuels potential of newly isolated <i>Scenedesmus</i> sp. from desert Cholistan
Bibliographic record
Abstract
Abstract Microalgae have an excellent potential for producing valuable natural products, including biofuels. Therefore, it is imperative to explore and document the existing microalgal flora and utilize their potentials to cope the increasing human needs. The present work aims at exploring and characterizing newly isolated microalgae from desert Cholistan, a habitat with myriad algal diversity. Light microscopy, scanning electron microscopy, and molecular phylogenetic approaches were used for species‐level identification. Characterization and growth optimization of Scendesmus sp. were analyzed under three different growth modes to determine the most favorable conditions for increasing biomass, growth rate, and lipid content. The results revealed that mixotrophic (MT) mode significantly increases photosynthetic activity, growth rate, and lipid content with glycerol as supplement carbon source. The investigated Scenedesmus dimorphous produced a maximum dry weight of 1.73 g L−1, improved fatty acid methyl esters profile and yield lipid up to 40% of DCW (68 g L−1) under MT mode, which is almost double to that of photoautotrophic cultivation. The glycerol availability in medium has been identified as the critical element for boosting growth and lipid content. Thus, it can reduce the cost of biofuel production.
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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".