Dynamic digital image analysis: emerging technology for particle characterization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The feasibility of applying dynamic imaging analysis technology to particle characterization has been evaluated for application in the water sector. A system has been developed which captures in-situ images of suspended particles in a flowing sample stream and analyzes these images in real time to determine particle size and concentration. The technology can measure samples having a wide range of particle sizes (approximately 1.5 to 1,000 microm equivalent circular diameter) and concentrations (<1 to >1 million/ml). The system also provides magnified images of particles for visual analysis of properties such as size, shape and grayscale level. There are no sample preparation requirements and statistically accurate results are produced in less than three minutes per sample. The overall system architecture is described. The major design challenges in developing a practical system include obtaining adequate contrast for the range of particle materials found in typical water samples and achieving this under operating conditions permitting an adequate sample processing rate for real time feedback of results. Performance of the instrument is reported in reference to industry accepted particle standards and applications as an analytical tool for the water industries are considered.
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.
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 | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it