Assessment of FlowCam VisualSpreadsheet as a potential tool for rapid semi-automatic analysis of lacustrine Arcellinida (testate lobose amoebae)
Bibliographic record
Abstract
Arcellinida are an established group of bioindicators in lake studies, but conventional labor-intensive microscopic analysis techniques often limit the number of samples analysed.In this study, the FlowCam with VisualSpreadsheet (FCVS), a flow cytometer and microscope with machine learning software, was assessed as an instrument for rapid Arcellinida analysis.In a 2016 study, manual identification and quantification of Arcellinida was performed through conventional microscopy on 46 samples collected from Wightman Cove, Oromocto Lake, New Brunswick, Canada.The samples were reanalyzed by FCVS where Arcellinida were categorized into morphological classes.The datasets obtained through conventional microscopy and through FCVS were compared at the morphotype level using cluster and Bray-Curtis dissimilarity analyses.The methods produced highly similar arcellinidan assemblages that corresponded to specific lake habitats.FCVS was found to reduce analytical time by approximately 45%.FCVS shows potential as a reliable method for more rapid analysis of lacustrine Arcellinida; however, assemblage results can only be obtained at the morphotype level.Microscopic methods should still be used if species-level results are desired.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".