Fast & Flexible: streamlining a simulation- based approach to collision risk assessments
Bibliographic record
Abstract
It is critical that tools for assessing potential environmental impacts are, amongst other things, fit to reduce uncertainty and provide sufficient confidence to permit decision. To address collision risk between marine mammals and tidal energy devices a simulation-based approach was developed to create a robust system that can adapt to any typical scenario and include novel device designs and ecological parameters. The approach here makes use of an open-source game-engine, Blender, to simulate a tidal energy device, the animal, and its movement in 3D to calculate collision probabilities. This free-to-use software offers an economical solution, however, the complexity of simulating a 3D environment, and adapting game-design software for the purposes of environmental questions poses challenges such as the time required for simulations to complete and the computing power required (e.g. number of CPU cores). The aim of this current study was to streamline the simulation-based approach and outline a more efficient process so that the time to produce results is greatly reduced. Simulation runtime has been significantly reduced by employing increased parallelisation and enabling running the software on a high-performance computer. The end-to-end runtime was reduced by a factor of 17 to greatly improve efficiency. Further improvements to this simulation-based approach gives industry a greater number of options for robust quantification of collision risk and, consequently this work can aid regulators in making decisions during the consent, and post-consent phases of tidal energy developments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".