MIPS: A service-based aid for intelligence analysis
Bibliographic record
Abstract
The Management of Information Processing Services (MIPS) project has two main objectives; the notification to analysts of the arrival of relevant new information and the automatic processing of the new information. Within these objectives a number of significant challenges were addressed. To achieve the first objective, the team had to demonstrate the capability for specific analysts to be “tipped-off” in real-time that textual reports and sensor-data have been received that are relevant to their analytical tasks, including the possibility that such reports have been made available by other nations. In the case of the second objective, the team had to demonstrate the capability for the infrastructure to automatically initiate processing of input data as it arrives, consistent with satisfying the analytical goals of teams of analysts, in as an efficient a manner as possible (including the case where data is made available by more than one nation). Using the Information Fabric middleware developed as part of the International Technology Alliance (ITA) research program, the team created a service based information processing infrastructure to achieve the objectives and challenges set by the customer. The infrastructure allows existing software to be wrapped as a service and/or specially written services to be integrated with each other as well as with other ITA technologies such as the Controlled English (CE) Store or the Gaian Database. This paper will identify the difficulties in designing and implementing the MIPS infrastructure together with describing its architecture and illustrating its use with a worked example use case.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".