Abstract: Application of multibeam sonar technology for benthic habitat mapping in Newfoundland and Labrador
Bibliographic record
Abstract
In this paper we describe an approach to benthic habitat mapping by supervised classification of multibeam sonarderived data, with examples from coastal Labrador. Benthic habitat is a combination of seabed substrate and its associated biotic components. In our habitat mapping approach we assume that substrate controls the distribution of benthic biota, a relationship which has been established for nearshore and continental shelf environments elsewhere, and that substrate can be accurately mapped using acoustic seabed data acquired from multibeam sonar surveys. Multibeam data comprise both bathymetric and backscatter intensity values; the former provides information on water depth, slope angle, and general basin physiography, while the latter largely depends on seabed properties, such as texture and roughness and the occurrence of structure-forming biota. Multibeam data are ground-truthed using drop-video camera transects and benthic grab samples, with both video and grab sample data for each ground-truth point.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".