Editorial Single-Photon Technologies and Applications
Bibliographic record
Abstract
Single-photon technologies are vital to a broad range of research areas such as nuclear physics, astrophysics, biology and computer vision. Fields such as LiDAR, single-photon imaging, biomedical imaging, quantum communication, optical quantum computing, quantum metrology, remote sensing and non-line-of-sight (NLOS) imaging also benefit from developments in single-photon technologies. This special issue is intended to bring together a broad range of research article with interests in single-photon detectors, single photon avalanche diode (SPAD), single-photon sources, photon entanglement, NLOS imaging, computational imaging algorithms, and their general applications in quantum information science, 2D and 3D imaging, sensing and metrology. This special issue addresses the current progress and latest breakthroughs in single-photon technologies and their emergent applications, particularly covering the following areas:
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".