Acoustic seabed classification of marine habitats: studies in the western coastal-shelf area of Portugal
Bibliographic record
Abstract
Abstract Two single-beam, seabed-classification systems, QTC VIEW Series IV and QTC VIEW Series V, were used to identify and map biosedimentary gradients in a mid-shelf area off Western Portugal. The survey area has a moderate slope, a depth ranging from 30 to 90 m along a 3.5-km axis perpendicular to the shoreline, and is characterized by smooth sedimentary and biological gradients. Ground truth for sediment grain size and macrofaunal communities was based on grab sampling at 20 sites. The sedimentary and biological data were analysed using classification and ordination techniques. The acoustic data were analysed with qtc impact software and classified into acoustic classes. The affinity groups obtained in each data set were mapped using a Geographics Information System. All showed good agreement and identified prevailing gradients along a northwest–southeast direction. Three acoustic classes were identified, corresponding to the predominant sediment types, namely fine sand with low silt and clay content, silty, very fine sand, and mud. A close relationship with benthic communities was also verified, although less marked because benthic communities continuously change along the northwest–southeast gradient. Overall, the acoustic system coupled with ground-truthing data was able to discriminate and characterize the various benthic biotopes in the survey area.
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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 | 0.000 | 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) | 0.001 | 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".