JETGET - An analysis and visualization tool for (magneto-)hydrodynamic jet simulations
Bibliographic record
Abstract
In simulations of (magnetized-)fluid dynamics in physics and astrophysics, the visualization techniques are so frequently applied to analyse data that they have become a fundamental part of the research. Data produced is often a multi-dimensional set with several physical quantities, that are usually complex to manage and analyse. JETGET is a visualization and analysis tool we developed for accessing data stored in Hierarchical Data Format (HDF) and ASCII files. Although JETGET has been optimized to handle data output from jet simulations using the Zeus code from NCSA, it is also capable of analysing other data output from simulations using other codes. JETGET can select variables from the data files, render both two- and three-dimensional graphics and analyse and plot important physical quantities. Graphics can be saved in encapsulated Postscript, JPEG, VRML or saved into an MPEG for later visualization and/or presentations. An example of use of JETGET in analysing a 3-dimensional simulation of jets emanating from accretion disks surrounding a protostar is shown. The strength of JETGET in extracting the physics underlying such phenomena is demonstrated as well as its capabilities in visualizing the 3-dimensional features of the simulated magneto-hydrodynamic jets. The JETGET tool is written in Interactive Data Language (IDL) and uses a graphical user interface to manipulate the data. The tool was developed on a LINUX platform and can be run on any platform that supports IDL. JETGET can be downloaded (including more information about its utilities) from http://www.capca.ucalgary.ca/software.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.012 |
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".