Signal-to-noise ratio performance for detection systems of quantum dot multiplexed optical encoding
Bibliographic record
Abstract
The wavelength and intensity of the spectral emission of a group of quantum dots can be altered by varying the size of the quantum dots (wavelength) and the number of the quantum dots (intensity). In this way, information and be encoded into the spectral characteristics of the group of quantum dots emission. This approach has been proposed for the application of tagging thousands of biomolecules as well as replacing barcodes as a means to identify objects. The potential in this system rests in the ability to achieve a high information density. In this paper we model and measure the noise in the readout system that will contribute to the decrease of the information density. We also propose an alternate optical detector as a possibly simpler and cheaper design. Our results demonstrate that the signal-to-noise ratio for both the CCD and photodiode detectors has a linear relationship with time. To achieve comparable SNR, approximately 30dB, in both detectors we note that the CCD-based spectrometer requires integration times on the order of hundreds of milliseconds while the photodiode only requires tens of microseconds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".