Why this work is in the frame
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Bibliographic record
Abstract
The spontaneous imbibition of a liquid within porous media, known as wicking, can display uncommon features in textiles and yarns. Yarns exhibited step-wise wicking dynamics not captured by current models. HYPOTHESIS: Wicking dynamics in yarns not only depend on inter-fiber pore filling, but are mainly determined by the pore-to-pore transition processes and the structure of the pore network. EXPERIMENTS: Fast X-ray tomographic microscopy is employed to reveal the pore scale processes and neutron radiography for the macroscopic water uptake in yarns. A semi-empirical pore network model is presented that employs the measured pore network topology and pore scale dynamics to reproduce the experimentally observed wicking dynamics in yarns. FINDINGS: The yarn pore system is a sparse network of long and narrow pores that promotes step-wise uptake dynamics. Wicking in yarns displays fast pore filling events in the order of seconds and long waiting times between filling events up to several minutes while navigating the pore network. As main result, we find that a few filling events directly determine the macroscopic behavior of wicking in the sparse pore network of yarns. It is necessary to consider pore-to-pore transition waiting times and the pore network structure to explain the characteristics of wicking dynamics in yarns.
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 | 0.002 | 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) | 0.001 | 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 it