Diagnostic-based modeling on a micro-scale atmospheric-pressure plasma jet
Bibliographic record
Abstract
Diagnostic-based modeling (DBM) actively combines complementary advantages of numerical plasma simulations and relatively simple optical emission spectroscopy (OES). DBM is applied to determine spatial absolute atomic oxygen ground-state density profiles in a micro atmospheric-pressure plasma jet operated in He–O2. A 1D fluid model with semi-kinetic treatment of the electrons yields detailed information on the electron dynamics and the corresponding spatio-temporal electron energy distribution function. Benchmarking this time- and space-resolved simulation with phase-resolved OES (PROES) allows subsequent derivation of effective excitation rates as the basis for DBM. The population dynamics of the upper O(3p3P) oxygen state (λ = 844 nm) is governed by direct electron impact excitation, dissociative excitation, radiation losses, and collisional induced quenching. Absolute values for atomic oxygen densities are obtained through tracer comparison with the upper Ar(2p1) state (λ = 750.4 nm). The resulting spatial profile for the absolute atomic oxygen density shows an excellent quantitative agreement to a density profile obtained by two-photon absorption laser-induced fluorescence spectroscopy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".