Parametrical and Textural Analysis of Sidescan Sonar Images of the Seafloor
Bibliographic record
Abstract
This paper presents a comparative analysis of a new method of sidescan sonar imagery segmentation helpful in the estimation and spatial distribution of macrophytobentos in Arctic conditions. In our research, we designed three methods, two based on a parametric analysis of sidescan sonar echo signals and the third on a textural analysis relying on mathematical morphology. Acoustic observations were verified by video recordings and biological samplings. A single beam echosounder was also used. The Spitsbergen fjords represent a periglacial environment with great diversity of morphodynamic processes and sensitivity for global warming changes, so it is one of the most promising areas to study the influence of climate change on an ecosystem. Sidescan sonar is a very effective and economic tool for mapping marine vegetation on the seafloor, but interpretation of data still causes many problems especially in the specific conditions of the Arctic fjords (underwater rocks, postglacial sediments, steep slopes). Proposed segmentation and classification algorithms enable estimation of the bottom area covered by algae and, together with results of biological sampling and single beam echosounder measurements, estimation of algae biovolume and biomass.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".