NATO Task Group on Information Fusion
Bibliographic record
Abstract
The Task Group on Information Fusion (TgonIF) is a task group affiliated to the NATO Research Technology Organisation (RTO) / Information Systems Technology (IST) Panel. The task group is addressing the importance and difficulty of fusing the always increasing variety and quantity of information produced by the full spectrum of sensors and sources during the ever changing type of military operations. The first part of this report is a slightly updated version of the official status report made by the chairman of the task group, Gaetan Thibault from DRDC (Valcartier), Canada, and presented at the 9th IST Panel Business meeting taking place 30-31 May 2002 in Estoril Portugal. The second part is a sample of examples taken from a course in Intelligence for OOTW given by WO1 Dave Steer, Defence Intelligence and Security Centre, Chicksands, UK. All names and references in these examples have been changed for security reasons.
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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.042 |
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".