Cytometric Analysis of Diverse Glaucophyte Species Reveals Distinctive Signals Useful for Fluorescence-Based Detection and Sorting
Bibliographic record
Abstract
Abstract Glaucophytes, red algae and viridiplants (green algae and land plants) are formally united in the supergroup Archaeplastida. Although diverse molecular and genomic evidence suggest the common origin of the three Archaeplastida lineages, the lack of a robust glaucophyte knowledgebase has limited comprehensive evaluations of competing hypotheses. Glaucophytes are rare and apparently confined to freshwater habitats. However, the distribution and diversity of these algae have not been thoroughly explored owing to challenges with detecting and isolating novel specimens. Here we examined the cytometric signatures of representative species of the genera Cyanophora , Cyanoptyche , Glaucocystis and Gloeochaete for a distinctive signal that would aid identification. Most glaucophytes analyzed presented a relatively high red fluorescence signal due to the presence of the blue phycobiliproteins C-phycocyanin and allophycocyanin. Cell-size differences and the concurrent presence of the red phycobiliprotein phycoerythrin in other algal lineages, such as red algae and cryptophytes, allowed us to distinguish glaucophytes from other photosynthetic cells containing blue phycobiliproteins. Our results indicate that the peculiar autofluorescence signal of glaucophytes will facilitate further identification and isolation on novel specimens of this scarce but important algal group.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".