Bibliographic record
Abstract
The term “microflows” is often associated with microfluidics, which is a multidisciplinary field intersecting engineering, physics, chemistry, microtechnology, and biotechnology that has emerged in the beginning of the 1980s. Improvements in fabrication of microflow devices such as micropumps, microvalves, microheat exchangers, and other microcomponents and sensors have led to the rapid development of microfluidics in the last decade (Morini, 2011). For this reason, many studies have been conducted to verify if the laws governing transport phenomena within channels of macroscopic dimensions are applicable at the microscale. These studies clearly show the necessity of correcting the classical theory when applying it to flow in microchannels, but appropriate corrections of the classical theory require reliable measurement of microflows. On the other hand, microflows also originate from a diffusive transport of fluids, typically gases, through seemingly non-porous media, such as perm-selective membranes, packaging materials, and walls of nuclear and chemical vessels. Characterization of materials for gas separation membranes, packages in food industry, walls of chemical and nuclear reactors, which involve determination of diffusivity, permeability and solubility coefficients of different gases in these materials, relies on accurate measurement of microflows.
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.
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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".