Data from: Constraining biospheric carbon dioxide fluxes by combined top-down and bottom-up approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Acknowledgements. We would like to thank Martin Jung, Jakob A. Nelson, Sophia Walther, and the FLUXCOM team for their structuralsupport, feedback and discussion. The Authors would like to thank the producers of the Inversion data included in this study: Ingrid Luijkxand Wouter Peters (CTE), Frederic Chevallier and the Copernicus Atmosphere Monitoring Service (CAMS), Christian Roedenbeck (JenaCarboscope sEXTocNEET), Yosuke Niwa (NISMON-CO2), and Liang Feng and Paul Palmer (UoE). This research was funded by theEuropean Research Council (ERC) Synergy Grant ’Understanding and modeling the Earth System with Machine Learning (USMILE)’under the Horizon 2020 research and innovation programme (Grant Agreement No. 855187) This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux(U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS,NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowl-edge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max PlanckInstitute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Depart-ment of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory,Microsoft Research eScience, Oak Ridge National Laboratory, University of California - Berkeley, University of Virginia
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| 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 it