A rate-of-rise facility for measuring properties of wick structures
Bibliographic record
Abstract
Abstract This work details a mass rate-of-rise (mROR) apparatus and analysis method for the accurate and precise determination of capillary wick parameters: permeability, K , effective pore radius, r eff , and porosity, ϵ . Three factors were examined: (a) the accuracy of the theoretical models and their curve-fitting approaches associated with the mROR technique, (b) the influence of the experimental procedure on repeatability, and (c) how the uncertainty of the experimental input parameters propagates through the data analysis procedure and compounds the overall uncertainty of the wick parameters ( K and r eff ). Four models and fittings methods were investigated: the Lucas–Washburn method, the gravity-based d m /d t method, the gravity-based t – m method, and the gravity-based m – t method. It is demonstrated that the m – t method developed here shows the lowest error and, equally importantly, that it is free of user decisions in the context of ‘data scrubbing’ because the entire mROR data set is used in its raw form. To test accuracy and repeatability, a precision-controlled mROR apparatus is proposed. Experiments were performed for commercially available wicks. A robust Monte Carlo error analysis method was developed and applied to quantify the overall uncertainty in the wick parameters as a function of the input uncertainties of all measured quantities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".