Monitoring indigenous microalgae using derivative spectrophotometry and comparison with <i>M. aeruginos</i>a and <i>C. vulgaris</i>
Bibliographic record
Abstract
Abstract Derivative spectrophotometry was investigated as a monitoring tool for indigenous microalgae in surface waters. Absorbance spectra of indigenous microalgae were studied at low, medium and high range concentrations and were compared to the absorbance spectra of pure strains of M. aeruginosa and C. vulgaris to understand the differences in their absorbance fingerprints and the applicability of this method for real-time monitoring. Method Detection Limit (MDL) of the indigenous microalgae sample from its absorbance spectra was found to be 158,693 cells/mL. First derivative spectrophotometry was effective in detecting mixed indigenous microalgae at medium and high concentrations; however, it failed to differentiate between noise and signal at low concentrations. Subsequently, Savitzky-Golay algorithm was applied to improve the sensitivity and specificity of detection. The Savitzky-Golay first derivative of absorbance resulted in distinctive peaks and spectra fingerprints, indicating it can be used not only to monitor but also to identify various species of microalgae in water bodies. The Savitzky-Golay first derivative of absorbance also resulted in the lowest detection limits (60,170 cells/mL). Derivative spectrophotometry, along with mathematical and statistical tools, can be used for real-time detection and monitoring of mixed indigenous microalgae in surface waters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".