Rapid in situ measures of phytoplankton communities using the bbe FluoroProbe: evaluation of spectral calibration, instrument intercompatibility, and performance range
Bibliographic record
Abstract
The FluoroProbe (FP; bbe Moldaenke, GmbH) is increasingly deployed as a means to measure in situ abundance and composition of phytoplankton communities, yet few rigorous evaluations have been made of its performance. In this study, phytoplankton strains were grown under standardized conditions to test FP performance across a range of biomass concentrations (0.2 to 20 μg chlorophyll a (chl a)·L−1). FP estimates of in vivo chl a were compared with chl a values from acetone extraction analysis. Overall, in vivo chl a detected by the FP was well correlated with extracted values (r2 = 0.85, n = 409, p < 0.0001). However, the difference between in vivo FP measures and the acetone extracted method was high in some cases. Accuracy did not change over a range of chromophoric dissolved organic matter concentrations. Calibration with each laboratory strain improved FP accuracy over the standard instrument factory settings. The performance of two different instruments was not significantly different (p = 0.38), showing good data intercompatibility. In the majority of experiments involving multiple phytoplankton groups at various proportions and concentrations, the FP resolved all groups. The FP is a cost-effective and practical method for identifying trends in phytoplankton dynamics but has limitations with respect to accurate total chl a measurement.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".