Bibliographic record
Abstract
Differential geometry is concerned with the calculus of smooth surfaces. The field rose to prominence through its incredible power to describe Einstein’s Theory of General Relativity, in which spacetime is considered to be a smooth four-dimensional manifold. More recently, differential geometry has been used to describe stress and strain for elastic bodies, in digital signals processing and in probability theory. Smooth surfaces appear in a variety of natural systems. This research focusses on the salvinia leaf, a floating fern whose skin has the unusual property that, when immersed in water, a stable, persistent air layer is retained on the surface of the leaf. This air-water interface is made possible by the phenomenon of surface tension and a forest-like structure on the surface of the leaf, which forms a ‘tent’ of air. This interface is a smooth surface that can be investigated using differential geometry, and has the particular property that it’s mean curvature is constant. The equation of capillary pressure, developed in the early nineteenth century by Thomas Young and Pierre-Simon Laplace, governs the system. However, the traditional formulation of the problem can be extremely difficult to solve. The project involved reformulating the Young-Laplace equation, a second-order nonlinear partial differential equation, in a coordinate independent fashion using differential geometry. Finite element analysis was then used to obtain numerical solutions for specific domain geometries. The ability to find domain geometries which form surfaces with specified properties will aid the design of effective bionic devices.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.418 | 0.114 |
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".