Enumerating viable phytoplankton using a culture-based Most Probable Number assay following ultraviolet-C treatment
Why this work is in the frame
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Bibliographic record
Abstract
Ballast water management systems (BWMS) must be tested to assess their compliance with standards for the discharge of organisms, for example in the ≥ 10- and < 50-μm size category, which is dominated by phytoplankton. Assessment of BWMS performance with the vital stains fluorescein diacetate + 5-chlorofluorescein diacetate, required by regulations in the USA, is problematic in the case of ultraviolet-C (UVC) radiation. This is because UVC targets nucleotides-and thus reproduction, hence viability-rather than membrane integrity, which is assayed by the stains. The Serial Dilution Culture-Most Probable Number (SDC-MPN) method, long used to enumerate fragile phytoplankton from natural communities, is appropriate for counting viable phytoplankton. We developed QA/QC "best practice" criteria for its application as a robust and repeatable assay of viable cells in cultures of phytoplankton before and after experimental treatment, then constructed dose-response curves for UVC-induced loss of viable cells in 12 species of phytoplankton from seven divisions. Sensitivity to UVC, expressed as the dose required to reduce viability by 99%-the criterion for type approval of treatment systems-varied more than 10-fold and was not correlated with cell size. The form of the dose-response curves varied between taxa, with most having a threshold dose below which there was no reduction in viability. Analysis of the patterns of growth indicates that if recovery from treatment occurred, it was complete in 1 or 2 days in > 80% of cases, long before the assays were terminated. We conclude that the SDC-MPN assay as described is robust and adaptable for use on natural phytoplankton.
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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 | 0.001 | 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) | 0.001 | 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 it