Recent progress on the development and analysis of the ITPA global H-mode confinement database
Bibliographic record
Abstract
This paper describes the updates to and analysis of the International Tokamak Physics Activity (ITPA) Global H-Mode Confinement Database version 3 (DB3) over the period 1994–2004. Global data, for the energy confinement time and its controlling parameters, have now been collected from 18 machines of different sizes and shapes: ASDEX, ASDEX Upgrade, C-Mod, COMPASS-D, DIII-D, JET, JFT-2M, JT-60U, MAST, NSTX, PBX-M, PDX, START, T-10, TCV, TdeV, TFTR and TUMAN-3M. The database now contains 10382 data entries from 3762 plasma discharges, including data from deuterium–tritium experiments, low-aspect ratio plasmas, dimensionless parameter experiments and plasmas. DB3 also contains an increased amount of data from a range of diverted machines and further data at high triangularity, high density and high current. A wide range of physics studies has been performed on DB3 with particular progress made in the separation of core and edge behaviour, dimensionless parameter analyses and the comparison of the database with one-dimensional transport codes. The errors in the physics variables of the database have also been studied and this has led to the use of errors in variables fits. A key aim of the database has always been to provide a basis for estimating the energy confinement properties of next step machines such as ITER, and so the impact of the database and its analysis on such machines is also discussed.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".