An Application of Automated Machine Learning Within a Data Farming Process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data farming is a simulation-based methodology used within the defense community to analyze complex systems and provide insights to decision makers. It can produce very large, multi-dimensional data sets that require sophisticated analysis tools, such as metamodeling. Advances in explainable artificial intelligence have expanded the types of metamodels that can be considered; however, constructing a well-fitting machine learning metamodel involves many tasks that can become time consuming for an analyst. Automated machine learning (autoML) can save an analyst time by automating metamodel training, tuning and testing. Using outputs of an agent-based simulation of a military ground-based air defense scenario, we compared the performance of metamodels trained using autoML and different experimental designs. We found that autoML can reasonably automate the construction of metamodels and adds robustness to the analysis by considering multiple types of metamodels; however, the type and size of experimental design can significantly impact metamodel performance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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