Digital in-line holographic microscopy applied to microfluidic studies
Bibliographic record
Abstract
Digital In-line Holographic Microscopy (DIHM) is a technique that provides depth and lateral resolution of the order of the wavelength throughout a volume of several cubic centimeters for visible light. This outstanding characteristic is reached by means of a simple optical setup and numerical reconstruction of the recorded holograms. It makes DIHM the right tool for applications in many microscopic studies. In this paper we study microfluidic phenomena by means of DIHM. To this end we seed a fluid with micron-size trackers (latex microspheres) and follow their displacement within an observation volume. We apply this technique to several situations such as the flow around a big sphere, flow through microchannels, bubbles in a fluid, bacterial motion in a diatom and the swimming behavior of paramecia and algae in water. By taking advantage in DIHM of the plane-to-plane reconstruction through a large depth of field, we generate 3D renderings of the paths followed by the trackers to produce a complete picture of the flow pattern, i.e. streamlines and velocity fields.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".