An overview of seabed-mapping technologies in the context of marine habitat classification☆
Bibliographic record
Abstract
Abstract A wide range of seabed-mapping technologies is reviewed in respect to their effectiveness in discriminating benthic habitats at different spatial scales. Of the seabed attributes considered important in controlling the benthic community of marine sands and gravel, sediment grain size, porosity or shear strength, and sediment dynamics were highlighted as the most important. Whilst no one mapping system can quantify all these attributes at the same time, some may be estimated by skilful interpretation of the remotely sensed data. For example, seabed processes or features, such as bedform migration, scour, slope failure, and gas venting are readily detectable by many of the mapping systems, and these characteristics in turn can be used to assist a habitat classification (and monitoring) of the seabed. We tabulate the relationship between “rapid” continental shelf sedimentological processes, the seabed attributes affecting these processes, and the most suitable mapping system to employ for their detection at different spatial scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".