Ancient life and moving fluids
Bibliographic record
Abstract
Over 3.7 billion years of Earth history, life has evolved complex adaptations to help navigate and interact with the fluid environment. Consequently, fluid dynamics has become a powerful tool for studying ancient fossils, providing insights into the palaeobiology and palaeoecology of extinct organisms from across the tree of life. In recent years, this approach has been extended to the Ediacara biota, an enigmatic assemblage of Neoproterozoic soft-bodied organisms that represent the first major radiation of macroscopic eukaryotes. Reconstructing the ways in which Ediacaran organisms interacted with the fluids provides new insights into how these organisms fed, moved, and interacted within communities. Here, we provide an in-depth review of fluid physics aimed at palaeobiologists, in which we dispel misconceptions related to the Reynolds number and associated flow conditions, and specify the governing equations of fluid dynamics. We then review recent advances in Ediacaran palaeobiology resulting from the application of computational fluid dynamics (CFD). We provide a worked example and account of best practice in CFD analyses of fossils, including the first large eddy simulation (LES) experiment performed on extinct organisms. Lastly, we identify key questions, barriers, and emerging techniques in fluid dynamics, which will not only allow us to understand the earliest animal ecosystems better, but will also help to develop new palaeobiological tools for studying ancient life.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".