Interpretation of total phytoplankton and cyanobacteria fluorescence from cross‐calibrated fluorometers, including sensitivity to turbidity and colored dissolved organic matter
Bibliographic record
Abstract
Abstract In vivo pigment fluorescence methods allow simple real‐time detection and quantification of freshwater algae and cyanobacteria. Available models are still limited to high‐cost fluorometers, validated for single instruments or individual water bodies, preventing data comparison between multiple instruments, and thus, restricting their use in large‐scale monitoring programs. Moreover, few models include corrections for optical interference (water turbidity and colored dissolved organic matter, CDOM). In this study, we developed simple models to predict phytoplankton and cyanobacterial chlorophyll a ( Chl a ) concentrations based on Chl a and C‐phycocyanin in vivo fluorescence, using multiple low‐cost handheld fluorometers. We aimed to: (1) fit models to mixed cyanobacterial and microalgal cultures; (2) cross‐calibrate nine fluorometers of the same brand and series; (3) correct the CDOM and turbidity effects; and (4) test the algorithms’ performance with natural samples. We achieved comparable results between nine instruments after the cross‐calibration, allowing their simultaneous use. We obtained algorithms for total and cyanobacterial Chl a estimation. We developed parametric corrections to remove CDOM and turbidity interferences in the algorithms. Five sampling sites (from a lake, a stream, and an estuary) were used to test the algorithms using eight cross‐calibrated fluorometers. The models showed their best performance after CDOM and turbidity corrections (total Chl a : R 2 = 0.99, RMSE = 7.8 μ g Chl a L −1 ; cyanobacterial Chl a : R 2 = 0.98, RMSE = 9.8 μ g Chl a L −1 ). In summary, our models can quantify total phytoplankton and cyanobacterial Chl a in real time with multiple low‐cost fluorometers, allowing its implementation in large‐scale monitoring programs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".