Tough2_MP: A parallel version of TOUGH2
Bibliographic record
Abstract
TOUGH2_MP is a massively parallel version of TOUGH2.It was developed for running on distributed-memory parallel computers to simulate large simulation problems that may not be solved by the standard, single-CPU TOUGH2 code.The new code implements an efficient massively parallel scheme, while preserving the full capacity and flexibility of the original TOUGH2 code.The new software uses the METIS software package for grid partitioning and AZTEC software package for linearequation solving.The standard message-passing interface is adopted for communication among processors.Numerical performance of the current version code has been tested on CRAY-T3E and IBM RS/6000 SP platforms.In addition, the parallel code has been successfully applied to real field problems of multi-million-cell simulations for threedimensional multiphase and multicomponent fluid and heat flow, as well as solute transport.In this paper, we will review the development of the TOUGH2_MP, and discuss the basic features, modules, and their applications.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".