Bibliographic record
Abstract
Fluid flows are typically classified as laminar or turbulent. While the glassy, regular flow of water from a slightly opened tap is laminar, the sinuous, irregular flow of water from a fully opened tap is turbulent. In a laminar flow, the velocity and other relevant fields are deterministic functions of position and time. Photos taken at different times, no matter how far removed, of steady laminar flow from a tap will be identical. In a turbulent flow, the velocity and other relevant fields manifest complex spatial and temporal fluctuations. A video of steady turbulent flow from a tap will exhibit a constantly changing pattern and many length and time scales. In nature and technology, laminar flows are more the exception than the rule. Fluvial, oceanic, pyroclastic, atmospheric, and interstellar flows are generally turbulent, as are the flows of blood through the left ventricle and air in the lungs. Flows around land, sea, and air vehicles and through pipelines, heating, cooling, and ventilation systems are generally turbulent, as are most flows involved in industrial processing, combustion, chemical reactions, and crystal growth. Over the past year, a significant portion of our research activity has focused on numerical studies of Navier-Stokes-αβ model and extensions thereof. Our results regarding these and other approaches to turbulence modeling are described below.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".