Bibliographic record
Abstract
Thoron (radon-220, 220Rn, half-life 55.6 s) is a useful aqueous tracer suitable for applications such as locating and measuring groundwater discharge in surface waters (including the coastal ocean) and detecting radium-224 (224Ra) bearing scale inside water pipes. Generally, such applications require only relative thoron measurements in the water. However, if a thoron-in-water quantification in absolute numbers is desired the knowledge of the thoron sensitivity of the measurement system is obligatory. Absolute readings would, e.g., give a measure of the 224Ra activity in the sediment, supporting the thoron, or of the quantity of 224Ra in the pipe scale. Since there is no standardised source of thoron-in-water (such as a NIST standard), there is no way to calibrate a thoron-in-water measurement system, in the usual sense. Up until now, therefore, it has not been possible to make absolute measurements of thoron in water. This paper presents a novel method of assessing the sensitivity of a mobile thoron-in-water measurement system. The paper analyses such systems and describes a straightforward experimental approach to obtain all setup-specific values of critical parameters that will allow a reasonably precise determination of the system thoron sensitivity. The method is simple enough that a thoron sensitivity calibration can be performed on site with no additional equipment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".