Mapping the deposition of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"> <mml:mrow> <mml:mmultiscripts> <mml:mtext>C</mml:mtext> <mml:mprescripts/> <mml:none/> <mml:mn>137</mml:mn> </mml:mmultiscripts> <mml:mtext>s</mml:mtext> </mml:mrow> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"> <mml:mrow> <mml:mmultiscripts> <mml:mtext>I</mml:mtext> <mml:mprescripts/> <mml:none/> <mml:mn>131</mml:mn> </mml:mmultiscripts> </mml:mrow> </mml:math> in North America following the 2011 Fukushima Daiichi Reactor accident
Bibliographic record
Abstract
The 2011 Fukushima Daiichi Reactor accident generated a large data set of global radionuclide observations. Frequent observations of xenon, caesium and iodine radioisotopes provided an opportunity to examine the performance of inter-continental scale meteorological models, in particular, the important mechanisms of in-cloud scavenging, precipitation, and deposition. Previous studies investigated these phenomena over short range, but this is the first time a global, coordinated surveillance system and in particular, a non-scavenged noble gas data set was available for use in such a study. Since particle size distributions are very different at long range, the parametrization of the deposition is important for accurate atmospheric modelling. The accuracy of these models are crucial in the Comprehensive Nuclear-Test-Ban Treaty (CTBT) context where discrimination of local and distant civilian sources from a potential nuclear test is a challenging problem. Beyond the CTBT context, accurate prediction of deposition is important for emergency and consequence management of nuclear emergencies, allowing a small set of data, combined with an appropriate model to represent a much larger domain, even up to continental scales. The modelling results for ground deposition and airborne activity of radiocaesium and radioiodine are presented and validated against the actual measurements.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.073 |
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".