Figure data for "Review of the Radon Tracer Method for GHG emission estimates: development, application guidelines, improvements, and caveats" by Chambers et al.
Bibliographic record
Abstract
This dataset is a summary of source data for all figures in the manuscript "Review of the Radon Tracer Method for GHG emission estimates: development, application guidelines, improvements, and caveats" submitted to Atmospheric Measurement Techniques in Oct 2025. Contributing authors: Scott D. Chambers1, Ute Karstens2, Alan D. Griffiths1, Stefan Röttger3, Arnoud Frumau4, Christopher T. Roulston5, Peter Sperlich6, Felix Vogel7, Agnieszka Podstawczyńska8, Dafina Kikaj9, Maksym Gachkivskyi10, Michel Ramonet11, Blagoj Mitrevski5, Janja Vaupotič12, Xuemeng Chen13, Annette Röttger3 1ANSTO Environment Research & Technology Group, Lucas Heights, 2234, Australia 2ICOS ERIC Carbon Portal, Lund University, Lund, 22362, Sweden 3Physikalisch-Technische Bundesanstalt, Braunschweig, 38116, Germany 4Netherlands Organisation for Applied Scientific Research (TNO), Petten, 1755 LE, Netherlands 5CSIRO Environment, Aspendale, 3195, Australia 6New Zealand Institute for Earth Science Ltd., Hataitai, Wellington, 6021, New Zealand 7Climate Research Division, Environment and Climate Change Canada (ECCC), Toronto, ON, M3H 5T4, Canada 8Department of Meteorology and Climatology, University of Lodz, Łódź, 90-139, Poland 9National Physical Laboratory, Teddington, TW11 0LW, UK 10Institut für Umweltphysik, Heidelberg University, Heidelberg, 69120, Germany 11Laboratoire des Sciences du Climat et de l'Environnement, Université Paris-Saclay, 91191 Gif-sur-Yvette, France 12Department of Environmental Sciences, Jožef Stefan Institute, Ljubljana, SI-1000, Slovenia 13Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Helsinki, FIN-00014, Finland
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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.637 | 0.351 |
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".