Three-dimensional thermophotonic super-resolution imaging by spatiotemporal diffusion reversal method
Bibliographic record
Abstract
Spatial super-resolution in thermophotonic imaging was achieved using a combination of spatial second-derivative forming, spatial gradient adaptive filtering, and Richardson-Lucy deconvolution in conjunction with the construction of an experimental point spread function. When implemented through enhanced truncation-correlation photothermal coherence tomography (eTC-PCT), it was possible to restore blurred infrared thermophotonic images to their prediffusion optical resolution state. This modality was tested in various biological applications and proved to be capable of imaging fine axial cracks in human teeth, well-patterned anatomical subsurface structures of a mouse brain, and neovascularization in a mouse thigh due to the rapid proliferation of cancer cells. This modality was found to be immune to optical scattering and could reveal the true spatial extent of biological features at subsurface depths that conventional thermal imaging cannot reach because of limitations imposed by the physics of spreading diffusion.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".