Sensitivity of infrared ship signature analysis to climatic data sampling methods
Bibliographic record
Abstract
Numerous studies have been conducted over the past decade to adequately sample the large amount of measured marine climate data for input to the design of a new naval surface combatant. Some of these involve the direct use of actual measured data (Vaitekunas and Kim, 2013) while others have reduced the complexity of the problem by focussing on the highly correlated data (e.g., air-sea temperature difference) while assuming the low to medium correlated data are simply uncorrelated (Cho, 2017). This paper will compare the two methods for a large data set off the Korean peninsula, spanning 5 years and 17 buoy locations. A follow-on analysis will compare the sensitivity of IR signature and IR susceptibility of a candidate ship (unclassified ShipIR model of a DDG class) to the variation in size, number of locations, and time span of the marine data being sampled.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".