MAPPING OF MARINE SOFT‐SEDIMENT COMMUNITIES: INTEGRATED SAMPLING FOR ECOLOGICAL INTERPRETATION
Bibliographic record
Abstract
Increasingly, knowledge of broad‐scale distribution patterns of populations, communities, and habitats of the seafloor is needed for impact assessment, conservation, and studies of ecological patterns and processes. There are substantial problems in directly transferring remote sensing approaches from terrestrial systems to the subtidal marine environment because of differences in sampling technologies and interpretation. At present, seafloor remote assessments tend to produce habitats predominantly based on sediment type and textural characteristics, with benthic communities often showing a high level of variability relative to these habitat types. Yet an integration of information on both the physical features of the seafloor and its ecology would be appropriate in many applications. In this study, data collected from a multi‐resolution nested survey of side‐scan, single‐beam sonar and video are used to investigate a bottom‐up approach for integrating acoustic data with quantitative assessments of subtidal soft‐sediment epibenthic communities. This approach successfully identified aspects of the acoustic data, together with environmental variables, that represented habitats with distinctly different epibenthic communities. The approach can be used, regardless of differences in data resolution, to determine location‐ and device‐specific relationships with the benthos. When such relationships can be successfully determined, marine ecologists have a tool for extrapolating from the more traditional small‐scale sampling to the scales more appropriate for broad‐scale impact assessment, management, and conservation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".