The serial dilution culture-most probable number assay to estimate phytoplankton concentrations in ballast water: comments and improvements
Bibliographic record
Abstract
In ecology, the enumeration of living aquatic unicellular protists is a century-old complicated task. The organisms are small, perhaps motile, and if they are non-motile their vitality is hard to gauge. A respectable technique still in use today is the Serial Dilution Culture-Most Probable Number (MPN) assay that determines the total viable phytoplankton concentration from the highest sample dilutions that exhibit chlorophyll-fluorescence after incubation. However, 99% of extant phytoplankton species have not been shown to grow in MPN tubes (false-negatives) and in ballast water applications, 10-50 µm species can be outcompeted by <10 µm autotrophs in abundance, biomass, and chlorophyll-fluorescence (false-positives). In addition, it is shown that after microscopic identification, contrary to established practice, the concentration of individual species or species-groups (10-50 µm) cannot be derived from standard MPN tables. Examples of a corrected derivation are given for simple (2-3 species) assemblages, but this correction becomes increasingly difficult in more complex mixtures. The MPN assay should not be used as a quantitative method in ecological studies or in applications such as ballast water testing.
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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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.040 |
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".