Extraction of chlorophyll a from Tetradesmus obliquus—a method upgrade
Bibliographic record
Abstract
Abstract Nowadays, the use of algae is prevalent for both industrial and agricultural purposes. The determination of chlorophyll (Chl) content is a commonly used method for estimating the phytoplankton abundance in different water bodies or biomass density of algal cultures. The aim of the present work is to optimise the efficiency of the Chl extraction from the green alga Tetradesmus obliquus using methanol as extracting solvent. The extraction efficiency was estimated by measuring the Chl a concentration of the extracts using fluorescence spectroscopy. To increase the extraction yield, glass fibre filters with algal cells on top were treated with 10% (v/v) formalin prior to the extraction. We found that this pretreatment significantly enhanced the extraction yield of Chl without its chemical decomposition. We also found that the optimal cell concentration for Chl determination ranged from 1.44 × 104 to 3.60 × 105 cells/mL and the extraction efficiency was lower when the cell density of the culture was out of this range. These results highlight the importance of the optimization of the pigment extraction for the studied algal species.
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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 | 0.001 |
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".