Bibliographic record
Abstract
Research on better methods to digitally represent microscopic specimens has increased over recent decades. Opaque specimens, such as microfossils and metallurgic specimens, are often viewed using reflected light microscopy. Existing 3D surface estimation techniques for reflected light microscopy do not model reflectance, restricting the representation to only one illumination condition and making them an imperfect recreation of the experience of using an actual microscope. This paper introduces a virtual reflected-light microscopy (VRLM) system that estimates both shape and reflectance from a set of specimen images. When coupled with anaglyph creation, the system can depict both depth information and illumination cues under any desired lighting configuration. Digital representations are compact and easily viewed in an online setting. A prototype used to construct VRLM representations is comprised only of a microscope, a digital camera, a motorized stage and software. Such a system automatically acquires VRLM representations of large batches of specimens. VRLM representations are then disseminated in an interactive online environment, which allows users to change the virtual light source direction and type. Experiments demonstrate high quality VRLM representations of 500 microfossils.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".