Major results of the MAARBLE project
Bibliographic record
Abstract
The goal of the MAARBLE (Monitoring, Analyzing and Assessing Radiation Belt Loss and Energization) project\nwas to shed light on the ways the dynamic evolution of the Van Allen belts is influenced by low-frequency electromagnetic\nwaves. MAARBLE was implemented by a consortium of seven institutions (five European, one Canadian\nand one US) with support from the European Community’s Seventh Framework Programme. The MAARBLE\nproject employed multi-spacecraft monitoring of the geospace environment, complemented by ground-based monitoring,\nin order to analyze and assess the physical mechanisms leading to radiation belt particle energisation\nand loss. Particular attention was paid to the role of ULF/VLF waves. Within MAARBLE we created a database\ncontaining properties of ULF and VLF waves, based on measurements from the Cluster, THEMIS and CHAMP\nmissions and from the CARISMA and IMAGE ground magnetometer networks. The database is now available to\nthe scientific community through the Cluster Science Archive as auxiliary content. Based on the wave database,\na statistical model of the wave activity dependent on the level of geomagnetic activity, solar wind forcing, and\nmagnetospheric region has been developed. Multi-spacecraft particle measurements have been incorporated into\ndata assimilation tools, leading to a more accurate estimate of the state of the radiation belts. The synergy of\nwave and particle observations is in the core of MAARBLE research studies of radiation belt dynamics. Results\nand conclusions from these studies will be presented in this paper. The MAARBLE (Monitoring, Analyzing and\nAssessing Radiation Belt Energization and Loss) collaborative research project has received funding from the European\nUnions Seventh Framework Programme (FP7-SPACE 2011-1) under grant agreement no. 284520.\n
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.037 |
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".