Bibliographic record
Abstract
Since the first spacecraft-based synthetic aperture radar (SAR) mission NASA’s SEASAT in 1978 radars have been flown in Low Earth Orbit (LEO) by other national space agencies including the Canadian Space Agency, European Space Agency, India Space Research Organization and the Japanese Aerospace Exploration Agency. Improvements in electronics, miniaturization and production have allowed for the deployment of SAR systems on aircraft for usage in agriculture, hazards assessment, land-use management and planning, meteorology, oceanography and surveillance. LEO SAR systems still provide a range of needful and timely information on large and small-scale weather conditions like those found across the Arctic where ground-base weather radars currently provide limited coverage. For investigators of solid-earth deformation attention must be given to the atmosphere on Interferometric SAR (InSAR) by aircraft and spacecraft multi-pass operations. Because radar has the capability to penetrate earth materials at frequencies from the P- to X-band attention must be given to the frequency dependent penetration depth and volume scattering. This is the focus of our new research project: to test the penetration depth of L-band SAR/InSAR by aircraft and spacecraft systems at a test site in Arctic Alaska using multi-frequency analysis and progressive burial of radar mesh-reflectors at measured depths below tundra while monitoring environmental conditions. Knowledge of the L-band penetration depth on lowland Arctic tundra is necessary to constrain analysis of carbon mass balance and hazardous conditions arising from permafrost degradation and thaw, surface heave and subsidence and thermokarst formation at local and regional scales.
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 | 0.000 | 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) | 0.001 | 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 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".