Sampling and analysis of microcystins: Implications for the development of standardized methods
Bibliographic record
Abstract
Microcystins (MC), a group of cyanotoxins, have been found in lakes and rivers worldwide. One goal of MC research is to develop models which predict MC concentrations, but these efforts have been hampered by a lack of standardized methods necessary for comparing data across studies. Here, we investigate the effect of chemical analysis (HPLC-PDA and ELISA), sample collection (whole water, plankton tow and surface scum), and choice of normalizing parameter (volume, dry weight, and chlorophyll a) on reported MC concentrations. Samples were collected over three years from a temperate mesotrophic, shallow lake with episodic blooms of cyanobacteria. We found that microcystins were up to four times higher in lake samples when analyzed by ELISA relative to HPLC-PDA and that MC concentration measured by HPLC explained less than half of the variation in MC concentrations measured by ELISA. Also, samples collected by plankton tow gave consistently higher concentrations than whole water samples. An additional HPLC analysis of two chlorophyte cultures revealed the presence of compounds with a similar UV absorbance spectrum to MC-LR, suggesting that identifying MC based solely on UV absorbance is not valid. Our results document the discrepancy in MC concentrations that can arise by using different methods throughout all stages of sampling, analysis, and reporting of MC concentrations.
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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 | 0.320 | 0.314 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| 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".