Bibliographic record
Abstract
On behalf of the DEAP collaboration SNOLAB and the McDonald Institute at Queen’s, the DEAP-3600 experiment employs a vessel filled with liquid argon to detect dark matter. [1] When argon atoms are excited by particle interactions, they emit ultraviolet light, which is subsequently detected by an array of sensors surrounding the vessel. This emitted light is analyzed to identify the nature of the interactions. During this summer, I had the opportunity to work on hardware upgrades for the detector, aimed at enhancing the filtration process to reduce background counts. This experience allowed me to gain a comprehensive understanding of the detector's hardware components and to physically install and assist in the installation of several key components. Following these upgrades, I analyzed a subset of the data collected by the detector during its vacuum stage. Utilizing tools such as DEAPDISPLAY, I conducted a detailed visual inspection of specific runs, manually categorizing events to improve our understanding and analysis of the data. This hands-on experience provided valuable insights into both the hardware and data analysis aspects of the detector system. [1] DEAP-3600 | SNOLAB
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".