Computerized Enumeration and Bio-volume Estimation of the Cyanobacteria Anabaena flos-aquae
Bibliographic record
Abstract
One of the most noticeable consequences of global climate change is the increased occurrence of algae and cyanobacteria blooms in surface waters. Some of these organisms may release hazardous toxins which represent a threat for human and animal health worldwide. Accordingly, the identification of threshold levels of toxic cyanobacteria cells has become common practice to ensure successful water management. The majority of current methods for cyanobacteria enumeration and bio-volume assessment are very time-consuming and costly. Furthermore, when dealing with multicellular organisms (i.e., filaments, colonies, agglomerates etc.), none of the existing enumeration methods can achieve good accuracy and all tend to underestimate cell concentrations and bio-volume. In this study, we introduce an integrated method for automated enumeration and bio-volume estimation of Anabaena flos-aquae, a common filamentous species of cyanobacteria often present in water blooms. Since Anabaena filaments are often long and tangled, a sample of its culture was first sonicated to isolate individual cells, and then imaged while being excited by a 546nm light source to considerably improve contrast. A probabilistic unsupervised classification was introduced to detect the target cells, and the size distribution of the cells was used for model calibration. Using this learned cell model, subsequent samples with natural Anabaena filaments were automatically enumerated and the bio-volume estimated. Compared to traditional manual enumeration using a hemacytometer, the developed method achieved equivalent accuracy in much less time, with less resources, and provided additional bio-volume information. These preliminary results demonstrate the potential of the developed method as a robust tool for water quality monitoring.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".