Bibliographic record
Abstract
The LINDSAY Virtual Human is a project that aims to design and simulate a human being in a virtual environment [1] . LINDSAY Composer is one of the components of the LINDSAY Virtual Human; it is a dedicated application that acts as a highly versatile simulation environment. The LINDSAY Virtual Nephron is a summer project that aimed to recreate the most basic functionality within a human nephron, while simultaneously ensuring that the mechanisms within the model were both accurate and reusable in other simulations. Using LINDSAY Composer as a test environment, two basic behaviours were developed for the virtual nephron: a tool to accommodate for ubiquitous flow, and selective permeability in the vessels of the nephron. The former is used to ensure that entities flowing through a complex vessel can navigate smoothly within the vessel walls. This was achieved using ray casting, a method for detection of nearby physical structures and adjusting the entity’s path to flow along the vessel in accordance with its distance from the vessel, as well as any forces acting on the entity. The latter selectively allows entities to pass from one vessel to another. Focus was placed on the relationship between ADH and water levels in the reabsorption model, osmotic pressure affected ADH release, which in turn altered membrane permeability. These simple functions were found to produce reasonably realistic behaviours for the entities in the simulation without the need for strictly defined behaviours through hard coding and scripted actions that are typical of most existing simulations. This allows the simulation to be highly modular and adaptable, in turn making it possible for these mechanisms to be reused and adapted for simulations in other regions of the body (such as circulation and digestion).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".