Topino: A Graphical Tool for Quantitative Assessment of Molecular Stream Separations
Bibliographic record
Abstract
In molecular-stream separation (MSS), a stream of a multi-component mixture is separated into multiple streams of individual components. Quantitative evaluation of MSS data has been a bottleneck in MSS for decades as there was no conventional way to present the data in a reproducible and uniform fashion. The roots of the problem were in the multi-dimensional nature of MSS data; even in the ideal case of steady-state separation, the data is three-dimensional: intensity and two spatial coordinates. We recently found a way to reduce the dimensionality via presenting the MSS data in a polar coordinate system and convoluting the data via integration of intensity along the radius axis. The result of this convolution is an angulagram — a simple 2D plot presenting integrated intensity vs angle. Not only does an angulagram simplify the visual assessment, but it also allows the determination of three quantitative parameters characterizing the quality of MSS: stream width, stream linearity, and stream deflection. Reliably converting an MSS image into an angulagram and accurately determining the stream parameters requires an advanced and user-friendly software tool. In this technical note, we introduce such a tool: the open-source software Topino available at https://github.com/Schallaven/topino. Topino is a stand-alone program with a modern graphical user interface that allows processing an MSS image in a fast (<2 min) and straightforward way. The robustness and ruggedness of Topino were confirmed by comparing the results obtained by three users. Topino removes the analytical bottleneck in MSS and will be an indispensable tool for MSS users with varying levels of experience. p { margin-bottom: 0.25cm; direction: ltr; line-height: 115%; text-align: left; orphans: 2; widows: 2 }a:link { color: #0000ff }
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.196 | 0.082 |
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".