Applicability of electrical resistance tomography to the analysis of fluid distribution in haemodialysis modules
Bibliographic record
Abstract
Abstract This work aims to explore the applicability of electrical resistance tomography (ERT) in the analysis of fluid distribution in haemodialysis modules, which is not straightforward due to the complex geometry of the hollow fibre bundles and the small sizes of the modules. On the other hand, ERT is potentially a suitable and convenient technique for investigation in this field due to its cost‐effectiveness and capacity to perform measurements in opaque systems. After a preliminary estimation of the fibre bundle local distribution, the assessment of the technique is performed by observing the time evolution of the measured conductivity maps during the module filling and emptying operations with water and air, which are alternatively fed inside or outside the fibre bundle. Reliable conductivity maps are obtained by placing the module vertically or horizontally. Additional experimental data collected by feeding liquid mixtures of different sodium chloride concentrations show that the technique is suitable for detecting concentration variations, due to the mass transfer through the fibres, and flow maldistribution, due to the specific geometry of the module. From the preliminary results collected in this work, the technique appears to be adequate for the collection of data that can support the optimization of the module geometry and computational model validation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".