A mixed framework to support heterogeneous collection asset scheduling
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
A new framework1 mixing evolutionary approach, discrete-event simulation and deep neural networks is proposed to achieve multi-asset collection/image acquisition scheduling in a surveillance context. It combines an extended graph-based hybrid genetic algorithm (GA) used for satellite image acquisition scheduling, with a predictive simulation-based deep neural network and knowledge-based capabilities to solve an heterogeneous collection asset scheduling problem. Plan execution simulation and neural networks predict track trajectories target behaviors. In contrast, a knowledge-based approach is used to estimate target identification. Both assessments are exploited to instantiate key solution quality parameters of a generalized decision model aimed at maximizing task collection value subject to a variety of collector capacity constraints. The mixed framework departs from basic point target/area coverage task modeling, introducing tracking and identification tasks while expanding resource allocation to various space, air and ground-based deployable image acquisition/collection asset types.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it