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
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
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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