Need for developing multi-band single and multiple pass POLinSAR monitoring platforms in air and space
Bibliographic record
Abstract
In this overview, reasons are provided on why we do need to place multi-modal, multi-band single and multiple pass POLinSAR monitoring platforms into air and space. The questions "on what POLinSAR monitoring can provide that POL-SAR and IN-SAR by themselves cannot accomplish" is assessed; whereupon facts and justifications on placing POL-IN-BISAR satellite clusters into space are presented. Reasons for this technology becoming a basic requirement for current, near-future and much more so for future all day & night year-round monitoring of the terrestrial covers are analyzed in view of the un-abating and uncontrollable terrestrial population explosion, which has, does and for ever will result in unavoidable conflicts deteriorating unfortunately at times into terrorism. The pertinent questions on how to reduce the exorbitant cost for initiating this "home-globe security protection" technology are therefore also broached, and the expected benefits are laid out. The pertinent National and International airborne and space borne multi-modal, multi-band SAR remote sensing and security conflict surveillance support agencies are herewith invited for co-sponsoring our proposal, which is timely and fleets of orbiting multi-band POLinSAR platforms are urgently required to be placed into space.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".