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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".