Introduction to Biophotonics
Bibliographic record
Abstract
Abstract The sections in this article are Definition of and General Introduction to Biophotonics Definition Visions Connected with Biophotonics Research Why Photons? Fields of Application and Technology Societal Relevance of Biophotonics Fighting Prevalent and Severe Diseases Unmet Medical Needs in T hird W orld Countries Economic Impact of Biophotonics Worldwide Research Activities in Biophotonics Biophotonics – a Cross‐Disciplinary Science Scientific Landscape International Conferences Scientific and Trade Journals Networks and Funding Programs Current Research Trends and Future Goals Photonic Methods for Biomedical Research Photonic Methods for Point‐of‐Care Diagnostics Photonic Methods for Clinical Imaging Photonic Methods for Therapeutic Applications Photonics in Pharmaceutics, Bioanalysis, and Environmental Research The B iophotonics4 L ife W orldwide C onsortium Mission and Purpose of the B iophotonics4 L ife W orldwide C onsortium History and Organization of the C onsortium Node Leaders for the C onsortium Current Activities of the BP 4 L ife C onsortium Medical F ellows Anticipated Role of Industry Potential Future Activities of the C onsortium
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.023 |
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".