Integrating Angular Backscatter Response Analysis Derivatives Into a Hierarchical Classification for Habitat Mapping
Why this work is in the frame
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Bibliographic record
Abstract
Accurate maps of biological communities are essential for monitoring and managing marine protected areas but more information on the most effective methods for developing these maps is needed. In this study, we use Wilsons Promontory Marine National Park in southeast Australia as a case study to determine the best combination of variables and scales for producing accurate habitat maps across the site. Wilsons Promontory has full multibeam echosounder (MBES) coverage coupled with towed video, remotely operated underwater vehicle (ROV) and drop video observations. Our study used an image segmentation approach incorporating MBES backscatter angular response curve and bathymetry derivatives to identify benthic community types using a hierarchical habitat classification scheme. The angular response curve data were extracted from MBES data using two different methods: 1) angular range analysis (ARA) and 2) backscatter angular response (AR). Habitat distributions were predicted using a supervised Random Forest approach combining bathymetry, ARA, and AR derivatives. Variable importance metrics indicated that ARA derivatives, such as grain size, impedance and volume heterogeneity were more important to model performance than AR derivatives mean, skewness, and kurtosis. Additionally, this study investigated the impact of segmentation software settings when creating segmented surfaces and their impact on overall model accuracy. We found using fine scale segmentation resulted in the best model performance. These results indicate the importance of incorporating backscatter derivatives into biological habitat maps and the need to consider scale to increase the accuracy of the outputs to help improve the spatial management of marine environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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