Bibliographic record
Abstract
The New Experiments With Spheres - Gas (NEWS-G) group at Queen's University uses Spherical Proportional Counters (SPCs) in the search for dark matter. Incoming radiation interacts with the gas inside the SPC through ionization, producing electron/ion pairs [1]. These primary electrons drift towards the central anode under the influence of an electric field [1]. A secondary ionization, termed avalanche, occurs at distances proportional to the size of the anode resulting in the multiplication of charges [1]. The signal that is produced is proportional to the number of secondary charges created during the avalanche [1]. A 30 cm-diameter sphere was used to characterize a six anode, spherical Bakelite prototype sensor. The sensor is divided into north and south three-anode read-out channels. To study the detector's response, an Fe55 source was placed inside the sphere, which was then filled with 1 bar of 40Ar+CH4 2% gas. The source was moved around the sphere with thirty-second data-taking runs taken at each point and high voltages applied to each channel. Cuts were applied to the event properties to isolate the 5.9 keV Fe55 peak. The peak was plotted as a histogram and fit with a Gaussian function with two additional linear terms. The gain and resolution of the sensor were extracted from the fit parameters. The channel dominance change angle was calculated by interpolating the number of events recorded by each channel at each point on the sphere. The gain, number of events and the channel dominance change angle were mapped on the sphere between 20 and 40 cm arc length in the theta direction, and all the way around the sphere in the phi direction to improve the understanding of the Bakelite sensor. References [1] G. Savvidis, "Sensor Characterization and Fiducial Volume Studies for the NEWS-G Dark Matter Experiment," Queen's University, Kingston, 2023.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".