Bibliographic record
Abstract
Experimental studies of un-bound nuclear states and nuclear reaction rates relevant for astrophysical processes are an important area of research with rare isotope beams. Both topics require the development of specialized experimental methods to study resonant reactions. The so-called active target approach, where the target material becomes part of the detection process, promises to combine high yields from thicker targets and low background with high resolution. This presentation will describe the implementation of the active-target technique in the ANASEN detector, which was developed by researchers from Louisiana State University and Florida State University. ANASEN was used in a number of stable and rare iosotope experiments in α– and proton scattering, as well as (α, p) and (d, p) reactions at FSU’s in-flight radioactive beam facility RESOLUT. ANASEN also was used to perform the first experiment, proton scattering off a 37K beam at the ReA3 facility. Another active-target detector with a very different approach is found in the Active Target Time-Projection Chamber, which was developed by a collaboration between researchers from MSU, the University of Notre Dame, Western Michigan University, LLNL, LBNL, and St. Mary’s University (Canada). First experiments with an AT-TPC prototype have been reported [1]. The talk will summarize the results from the first experiments with these systems, describe further development and future research projects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".