Topino: A Graphical Tool for Quantitative Assessment of Molecular Stream Separations
Bibliographic record
Abstract
<p><a></a> 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 <i>via</i> presenting the MSS data in a polar coordinate system and convoluting the data <i>via</i> 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 <u><a href="https://github.com/Schallaven/topino">https://github.com/Schallaven/topino</a></u>. 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> 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 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 | 0.000 |
| Open science | 0.000 | 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".