FEBIAD ionization development via a web-app for multidimensional characterization
Bibliographic record
Abstract
Abstract The ISAC-FEBIAD is an electron impact ion source typically used to ionize radioactive molecules or isotopes of elements beyond the reach of either surface or laser ion sources. The FEBIAD’s key tuning parameters are the cathode temperature defining the number of electrons created; the anode voltage establishing the electron energy; and the magnetic field controlling the electron density inside the anode volume. However, these parameters are typically scanned in a small and limited range when optimizing the source. Recent investigations have shown the need to explore the entire range of operational values accessible by the power supplies, not only due to the intrinsic variations from source to source but also to operate the source at optimal settings. To address this, a scanning algorithm has been implemented as a web interface thanks to the High-Level-Application (HLA) infrastructure available at TRIUMF. The ion beam intensity during both offline and online commissioning of the web app are presented here as contour plots. The optimal settings found for stable 20Ne are confirmed as the optimal settings for radioactive 18Ne. The main takeaway, however, is that the optimal ion source parameters differ between singly-charged, doubly-charged, and molecular species. This development demonstrate and facilitate the need for element and charge state-specific parameter optimization. Additionally, the results highlight the possibility of parameter optimization to enhance the ratio of the species of interest to co-ionized contamination.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.054 | 0.022 |
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".