Semi-Automated Equipment for CO<sub>2</sub> Purification and Graphitization at the A.E. Lalonde AMS Laboratory (Ottawa, Canada)
Bibliographic record
Abstract
Abstract New computer-controlled, semi-automatic systems were designed and built for CO2 purification and graphitization at the A.E. Lalonde Accelerator Mass Spectrometry (AMS) Laboratory with consideration for user friendliness and high throughput. The stainless steel vacuum lines are orbitally welded to ensure clean seams with low memory. The insulated graphitization ovens with plug-in electrodes provide a hazard-free environment for operators. The closed-loop cooling system circulating low-viscosity Dynalene at –40°C provides highly efficient water trapping. The LabVIEWTM software features (1) pressure and temperature recording for QA/QC; (2) safety interlocks to preclude operator errors resulting in sample loss, cross-contamination, or damaging a vacuum pump; and (3) automation for leak checking, iron conditioning, and running samples. Results from the first year of routinely measured standards, reference, and background materials are reproducible and within acceptance values. In the first year of operation (commissioned in spring 2014), over 1000 targets (~60% unknowns) were produced. With new tube sealing and CO2 purification lines, and two more graphitization lines now operational, the Lalonde AMS Laboratory is able to provide routine radiocarbon analysis (>200 µg carbon) at a capacity of more than 7000 targets per year. Most importantly, the equipment is safe and intuitive, making it ideal for education and training students to run their own samples.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".