Remote Sensing Data:Some Critical Comments on the Current State of Regulation and Reflection on Reform
Bibliographic record
Abstract
The authors provided an overview of several aspects of the legal protection of satellite remote sensing images. After referring to some national legislations and space policies for remote sensing distribution of spatial systems (US, Russian Fed., Canada, EU, India), the authors addressed the different legal protection formulas used in distribution agreements (copyright, EU Database Protection, classified information, etc.). Dr. Smith and Ms. Doldyrina questioned the applicability of the copyright formula to automatically generated data. In respect to EU Database Protection, the authors referred to several decisions of the European Court of Justice, which held that “...a right cannot be derived from the mere creation of a database”. The authors found highly questionable that under those Court decisions such protection applies to remote sensing operators, who only invest in creating a database. They proposed to create precise and clear definitions of remote sensing products, to identify proper legal protection for each of those products and to internationally harmonize the different licensing approaches. For this task, they suggested UNIDROIT as the forum to draft a model law.
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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 | 0.039 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.049 | 0.045 |
| Insufficient payload (model declined to judge) | 0.010 | 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".