SONAR BACKSCATTER DIFFERENTIATION OF DOMINANT MACROHABITAT TYPES IN A HYDROTHERMAL VENT FIELD
Bibliographic record
Abstract
Over the past 20 years, sonar remote sensing has opened ways of acquiring new spatial information on seafloor habitat and ecosystem properties. While some researchers are presently working to improve sonar methods so that broad-scale high-definition surveys can be effectively conducted for management purposes, others are trying to use these surveying techniques in more local areas. Because ecosystem management is scale-dependent, there is a need to acquire spatiotemporal knowledge over various scales to bridge the gap between already-acquired point-source data and information available at broader scales. Using a 675-kHz single-pencil-beam sonar mounted on the remotely operated vehicle ROPOS, 2200 m deep on the Juan de Fuca Ridge, East Pacific Rise, five dominant habitat types located in a hydrothermal vent field were identified and characterized by their sonar signatures. The data, collected at different altitudes from 1 to 10 m above the seafloor, were depth-normalized. We compared three ways of handling the echoes embedded in the backscatters to detect and differentiate the five habitat types; we examined the influence of footprint size on the discrimination capacity of the three methods; and we identified key variables, derived from echoes that characterize each habitat type. The first method used a set of variables describing echo shapes, and the second method used as variables the power intensity values found within the echoes, whereas the last method combined all these variables. Canonical discriminant analysis was used to discriminate among the five habitat types using the three methods. The discriminant models were constructed using 70% of the data while the remaining 30% were used for validation. The results showed that footprints 20-30 cm in diameter included a sufficient amount of spatial variation to make the sonar signatures sensitive to the habitat types, producing on average 82% correct classification. Smaller footprints produced lower percentages of correct classification; instead of the habitat types, the sonar data responded to intrapatch roughness and hardness characteristics. The sonar variables used in this study and the methods for extracting and transforming them are fully described in this paper and available in the public domain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".