Classification of phytoplankton cells as live or dead using the vital stains fluorescein diacetate and 5‐chloromethylfluorescein diacetate
Bibliographic record
Abstract
Regulations for ballast water treatment specify limits on the concentrations of living cells in discharge water. The vital stains fluorescein diacetate (FDA) and 5-chloromethylfluorescein diacetate (CMFDA) in combination have been recommended for use in verification of ballast water treatment technology. We tested the effectiveness of FDA and CMFDA, singly and in combination, in discriminating between living and heat-killed populations of 24 species of phytoplankton from seven divisions, verifying with quantitative growth assays that uniformly live and dead populations were compared. The diagnostic signal, per-cell fluorescence intensity, was measured by flow cytometry and alternate discriminatory thresholds were defined statistically from the frequency distributions of the dead or living cells. Species were clustered by staining patterns: for four species, the staining of live versus dead cells was distinct, and live-dead classification was essentially error free. But overlap between the frequency distributions of living and heat-killed cells in the other taxa led to unavoidable errors, well in excess of 20% in many. In 4 very weakly staining taxa, the mean fluorescence intensity in the heat-killed cells was higher than that of the living cells, which is inconsistent with the assumptions of the method. Applying the criteria of ≤5% false negative plus ≤5% false positive errors, and no significant loss of cells due to staining, FDA and FDA+CMFDA gave acceptably accurate results for only 8-10 of 24 species (i.e., 33%-42%). CMFDA was the least effective stain and its addition to FDA did not improve the performance of FDA alone.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".