Scanning setup for the investigation of fluorescence beam spectra
Bibliographic record
Abstract
In this paper we describe the scanning setup for investigating refracted beam spectra from changed cancer tissues. A special mechanical construction enables us to position measurement sensors using step motors and a micrometric XY stage. A fiber sensor which has been made of special fiber that does not provide any self fluorescence has been used for the illumination and detection. Full Text: PDF References: B. W. Chwirot, W. Jedrzejczyk, Luminescencja tkanek – nowe narzedzie wykrywania i lokalizacji nowotworow, Torun (1995). J. A. Kiernan, M. Wessendorf, Autofluorescence:Causes and cures, Toronto Western Research Institute, [DirectLink] B. Valeur, Molecular fluorescence – Principles and applications, Wiley – VCH, (2001). H. Zeng, A. McWillimas, S. Lam, Optical spectroscopy and imaging for early lung cancer detection, Photodiagnosis and Photodynamic Therapy 1, 111-122 (2004). [CrossRef] W. Denk, J. Strickler, W. W. Webb, Two-Photon Laser Scanning fluorescence Microscopy, Science 248, 73-76 (1990). [CrossRef] B. A. Flusberg, E. D. Cocker, W. Piyawattanametha, J. C. Jung, E. L. M. Cheung, M. J, Schnitzer, Fiber-optic fluorescence imaging, Nature Methods 2, 12 (2005). [CrossRef] J. W. Lichtman, J. A. Conchello, Fluorescence microscopy, Nature Methods 2 (2005). [CrossRef]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".