Impact of Flinch Technology on Damage Control and Survivability
Bibliographic record
Abstract
When a mission critical naval vessel is operating in dangerous waters or in battle, amongst other things, the success of its mission is a measure of capability and availability of its Weapon Systems, Combat and Communications Systems, Battle Damage Control System (BDCS) and Situational Awareness, as well as, its ability to recover from unplanned incidents. The next Generation Integrated Platform Management Systems (IPMS) for Autonomous Ships with much reduced manning, dictates special considerations for autonomous control systems across the ship support systems and beyond without need for man-in-the-loop for decision making. This entails detailed analysis, vulnerability and recoverability assessments during target ship’s basic design and the application of Artificial Intelligence (AI) where available. The optimum strategy involves consideration of distributed smart agent based control and monitoring systems that shall react rapidly to changes in operational demands and incidents without the need for man-in-the-loop, creating BDCS dynamic kill cards across ship subsystems and, extending the IPMS BDCS capabilities to Combat Management. The above gives rise to consideration of “Flinch Technology (FT)” [7]. It implies distributed smart agent based control systems that instinctively reacts to incidents for fast recoverability in the event of damage to supervisory control system (i.e. IPMS) and its related data communication network. This paper addresses the benefits that might be gained as a result of consideration of smart agent based control systems with no manin-the loop involvement for decision making. Such technology solutions, empowered by Artificial Intelligence (AI) could be adopted in the future Autonomous Combatant Ships.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".