Optimisation of naval gun firing patterns for engagement of manoeuvring surface tagrgets
Why this work is in the frame
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Bibliographic record
Abstract
The problem of determining optimal naval gun firing patterns for engagement of manoeuvring surface targets using traditional simulation approaches is computationally intensive, particularly for large salvo sizes. A simplified modelling technique based on representing warhead effects using Gaussian function approximations calibrated from more detailed modelling is reported here. The simplified model permits the parameter space defining lay-down of rounds in a firing pattern to be searched so as to determine optimal patterns that maximise salvo probability of kill. The method employs Newton’s method to formulate a system of equations defining local extrema, which are then solved using Gaussian elimination. These extrema are then searched to obtain the pattern that maximises salvo kill probability. This paper presents the underlying theory and gives initial results obtained using the model calibrated for an illustrative example from a more detailed model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it