Feature‐Driven Generalization of Isobaths on Nautical Charts: A Multi‐Agent System Approach
Why this work is in the frame
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Bibliographic record
Abstract
A nautical chart provides a schematic view of the seafloor where isobaths (contour lines joining points of same depth) and depth soundings are generalized to highlight undersea features that form navigational hazards and routes. Considering that the process is ultimately driven by features and their significance to navigation, this article proposes a generalization strategy where isobath generalization is controlled by undersea features directly. The seafloor is not perceived as a continuous depth field but as a set of discrete features composed by groups of isobaths. In this article, generalization constraints and operators are defined at feature level and composed of constraints and operators applying to isobaths. In order to automate the process, a multi‐agent system is designed where features are autonomous agents evaluating their environment in order to trigger operations. Interactions between agents are described and an example on a bathymetric database excerpt illustrates the feasibility of the approach.
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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