1 - Segmentation bathymétrique d'images multispectrales SPOT
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
This paper addresses the analysis of multispectral SPOT images in order to update nautical charts and to control nautical data. We have developed a segmentation approach based on two Markovian modeling steps. The first one is based on Markov chain (1D) modeling, whereas the second step involves a hierarchical process, Markovian in scale. Each of them includes the unsupervised estimation of the parameters. The model parameters are automatically calibrated whereas the noise parameters are estimated in the context of generalized distribution mixtures. An adaptive bathymetric inversion model is then derived in order to recover the water depth near the coasts. This bathymetric estimation has been validated on real data, for which control points are available that correspond to bathymetric measures supplied by previous hydrographic campaigns.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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