New tools for confocal macroscopy at OCI
Bibliographic record
Abstract
Light microscopy is a widely used tool in biomedical research. Fluorescence microscopy concentrates on quantitative and qualitative measurements of the fluorescent light emitted from the specimen under study. This is generally done using fluorescent molecules that can be tagged to antibodies, giving specific information about the micro-environment of the sample, i.e. oxygen concentration (tissue hypoxia), and/or to visualize specific structures, such as, tissue morphology (H & E), and blood vessel location (CD31). Biological applications of fluorescence microscopy such as imaging cut and stained tissue/tumour sections use specimens that 'overfill' the field of view of standard microscope objectives. An average tumour in these studies is 5-10 mm wide, while microscope objectives range in their field of view from -1 mm down to a few hundred microns, with smaller fields as magnification power increases. This can pose some difficulties for studies that look at the expression of a parameter across the entire specimen.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.112 | 0.055 |
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".