Sampling and analysis of microcystins: Implications for the development of standardized methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it