Sediment classification based on repetitive multibeam bathymetry surveys of an offshore disposal site
Bibliographic record
Abstract
A disposal site near Saint John, New Brunswick, Canada, has been surveyed at six-month intervals for the past three years with Simrad EM3000 multibeam bathymetry systems. Analysis of the bathymetry data from surveys in the autumn show accumulation of sediments over the site due to disposal activities in the summer and autumn; surveys in the spring show that the sediment has undergone transport and redistribution due to the effects of currents and waves over the winter months. Quantitative measures of sediment accumulation and erosion have been made and show definite changes in the nature and distribution of the disposal pile. Acoustic backscatter data collected simultaneously with four of these surveys were analyzed with the QTC MULTIVIEW/spl trade/ software, which segmented the multibeam sonar images into six regions of homogeneous acoustic character. The Multiview process leading to the six classes is described. Seafloor grab samples and photographs were used to characterize these acoustic classes physically. The depths and the classes were interpolated onto the same grid to allow subtractions and comparisons. Interpolating classes was done with a categorical interpolation process. Comparisons among the sequence of maps of acoustic classes show the spreading of new spoils and consistent seasonal changes over large areas. Because high-frequency backscatter depends heavily on surface conditions, seasonal changes in the local hydrodynamic regime could be responsible for these changes in acoustic character.
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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 | 0.000 |
| 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".