On the use of the serial dilution culture method to enumerate viable phytoplankton in natural communities of plankton subjected to ballast water treatment
Bibliographic record
Abstract
Discharge standards for ballast water treatment (BWT) systems are based on concentrations of living cells, for example, as determined with vital stains. Ultraviolet radiation (UV) stops the reproduction of microorganisms without killing them outright; they are living, but not viable, and ecologically as good as dead. Consequently, UV-treated discharge can be compliant with the intent of regulation while failing a live/dead test. An alternative evaluation of BWT can be proposed based on the assessment of viable, rather than living, cells in discharge water. In principle, the serial dilution culture-most probable number (SDC-MPN) method provides the appropriate measure for phytoplankton. But, the method has been criticized, particularly because it is thought that many phytoplankton species cannot be cultured. A review of the literature shows that although SDC-MPN has been used for more than 50 years-generally to identify and count phytoplankton species that cannot be preserved-its application to enumerate total viable phytoplankton seems to be new, putting past criticisms of the method in a different light. Importantly, viable cells need to grow only enough to be detected, not to be brought into sustained culture, and competition between species in a dilution tube is irrelevant as long as the winner is detectable. Thorough consideration of sources of error leads to recommendations for minimizing and quantifying uncertainties by optimizing growth conditions and conducting systematic comparisons. We conclude that with careful evaluation, SDC-MPN is potentially an effective method for assessing the viability of phytoplankton after BWT.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".