płk dr inż. Jarosław WIŚNIEWSKI
Bibliographic record
Abstract
In the light of revision of Polish national doctrine D/2 Rozpoznanie wojskowe (Military Intelligence) it is worth to reconsider not only specific issues, what military society expects but also general conditions. As such, intelligence principles are found. The basis for considerations presented in the article are doubts connected with full implementation of Allied intelligence principles for national purposes in the original release of the doctrinal document. Is this what we consider as an interoperability? What about other national examples? The article articulates review of Polish, NATO, United States and Canada solutions to build up a set of requirements/rules/principles of intelligence as a basis to develop own (national) solutions. Key words – intelligence, principles, interoperability, NATO and national approaches. Obecne prace nad nowelizacją doktryny D/2 Rozpoznanie wojskowe skłaniają do refleksji na jej zawartością i kierunkami zmian. Przyjęte bowiem i w tej chwili obo� wiązujące rozwiązania ograniczają się w dużym zakresie do „suchego ” przełożenia rozwiązań sojuszniczych i próby zaadaptowania ich na grunt narodowy. Skutek nie wydaje się pozytywny, gdyż po upływie niespełna roku od jej wprowadzenia rozpo�
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.034 |
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".