Passive illumination info retrieval used for status identification
Bibliographic record
Abstract
The secure information retrieval technologies are critical for status identification, particularly in the battlefield for friend/enemy discrimination. The materials or devices used in these technologies should be hard to find, difficult to counterfeit and as simple as possible. Moreover, if the coding information is totally position-invariant, i.e. neither sequence nor pixel based, it will greatly simplify the retrieval system. We describe an information retrieval technology, which possesses the above-mentioned features. The information is encoded by using luminescent semiconductor nanocrystals (or quantum-dots, QDs) mixed with a transparent solution, namely info-ink. When an exciting light beam shines at the info-ink, its emitting spectral features, i.e., wavelength and intensity, provide the encoded information. The info-ink could be applied on any kind of surface, for examples, document cover, top area of helmet, and even a fingernail. It is actually a thin layer of paint and requires no power supply. The retrieval device consists of an exciting light source, a mini-spectrometer and a data processing unit. However, for battlefield applications, a major problem with fluorescence-based technology is that the brightness of sunlight can overwhelm most reflected fluorescent signal. To overcome the shortcomings, the quantum dots are engineered to fluoresce at wavelengths corresponding to the absorption lines of the solar spectrum, more commonly known as Fraunhofer lines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".