A Comprehensive Spectral Analysis of the Auto-fluorescence Characteristics of Three Algae Species at Twelve Discrete Excitation Wavelengths
Bibliographic record
Abstract
Harmful algae blooms are a growing concern around the globe, and therefore fast and reliable methods to detect and classify different types of algae in an automatic manner is highly desired. In this study, we explore the auto-fluorescence characteristics of different algae types to determine whether using their auto-fluorescence spectra could be used in automatic identification. Preliminary experimental results in this study for generating auto-fluorescence spectra of Anabaena flos-aquae (Cyanophyta phylum), Ankistrodesmus falcatus (Chlorophyta phylum), and Euglena gracilis (Euglenozoa phylum) demonstrate that this information could potentially be leveraged to discriminate between different types of algae. Future work must be done to explore the auto-fluorescence spectra of additional species within a given genus in order to determine the intra-class and inter-class variability of these auto-fluorescence spectra. Future work also includes using this technique to determine the concentration in a mixed sample, as well as determining the robustness in a sample with contaminants. Future work will also involve exploring the use of additional fluorescent images, absorption spectra, and morphological features to improve the performance of the classifiers. Finally, as data collection continues we will explore using data augmentation to deal with unbalanced class sizes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".