Assessment of Metaborate Fusion for the Rapid Dissolution of Solid Samples: Suitability with the Northstar ARSIIe
Bibliographic record
Abstract
Abstract : The original goal of this project was to develop a rapid dissolution methodology for solid environmental samples and a crude pre-concentration of actinides (uranium, plutonium, thorium, americum) following the dissolution process to enable compatibility with the Automated Radionuclide Separation System-Environmental (ARSIIe) from Northstar Engineered Technologies. It was later required by the client during a subsequent meeting (August 2012, Quebec City, Quebec, Canada) that strontium be included in the list of analytes for which the sample preparation methodology should be applicable. This project was accomplished by executing a rigorous research and development strategy for soil dissolution that focuses on field deployment, efficiency, and the resulting dissolution being compatible with radiochemical separation performed by the ARSIIe using compatible designed protocols (necessary for controls and reproducibility). It was required by the client that the method of dissolution be simple (field-deployable), reliable, complete, and contained in as small a volume as possible (10- to 20-mL target). In addition, up to 1 gram of solid environmental samples needed to be solubilized through a lithium metaborate fusion technique, using the M4 Fluxer unit from Corporation Scientifique Claisse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".