Multispectral digital holographic microscopy with applications in water quality assessment
Bibliographic record
Abstract
Safe drinking water is essential for human health, yet over a billion people worldwide do not have access to safe drinking water. Due to the presence and accumulation of biological contaminants in natural waters (e.g., pathogens and neuro-, hepato-, and cytotoxins associated with algal blooms) remain a critical challenge in the provision of safe drinking water globally. It is not financially feasible and practical to monitor and quantify water quality frequently enough to identify the potential health risk due to contamination, especially in developing countries. We propose a low-cost, small-profile multispectral (MS) system based on Digital Holographic Microscopy (DHM) and investigate methods for rapidly capturing holographic data of natural water samples. We have developed a test-bed for an MSDHM instrument to produce and capture holographic data of the sample at different wavelengths in the visible and the near Infra-red spectral region, allowing for resolution improvement in the reconstructed images. Additionally, we have developed high-speed statistical signal processing and analysis techniques to facilitate rapid reconstruction and assessment of the MS holographic data being captured by the MSDHM instrument. The proposed system is used to examine cyanobacteria as well as Cryptosporidium parvum oocysts which remain important and difficult to treat microbiological contaminants that must be addressed for the provision of safe drinking water globally.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".