Ediacaran marine animal forests and the ventilation of the oceans
Bibliographic record
Abstract
This dataset consists of original data from the following paper: Gutarra, S., Mitchell, E.G., Dunn, F.S., Gibson, B.M., Racicot, R.A., Darroch, S.A.F. and Rahman, I.A. 2024. Ediacaran marine animal forests and the ventilation of the oceans. Current Biology 34, 2528–2534. doi:10.1016/j.cub.2024.04.059 The dataset comprises two ZIP files: Gutarra_et_al_Simulated_Communities.zip. This file includes spreadsheets (.xlsx) and text files (.txt) with details of the virtual communities simulated based on data on the size, density, spatial distribution, and orientation of Ediacaran fossils from the ‘D’, ‘E’, and Lower Mistaken Point (LMP) surfaces at Mistaken Point, Newfoundland, Canada. Virtual communities were simulated using the package spatstat in R. Gutarra_et_al_CFD_Simulations.zip. This file includes three-dimensional digital models (.stp) of the simulated communities and results files (.mph) from computational fluid dynamics (CFD) simulations of water flow using these models. Digital models were built in Rhinoceros 3D and CFD simulations were performed in COMSOL Multiphysics. The code used for spatial ecological modelling is available at: https://github.com/egmitchell/SpatialSimulations
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".