The Use of Subbottom Profiling in Refining Dredge Cuts for a Large‐Scale Sediment Remediation Project
Bibliographic record
Abstract
A number of different techniques were employed to locate the target dredge grade on a large‐scale Canadian sediment remediation project. These techniques included various coring events, Seabed Terminal Impact Newton Gradiometer (STING) testing, and geotechnical borings. Despite these techniques, the data set for interpolation of the dredge grade was widely spaced, and some of the investigations were not specifically intended to be used for defining the dredge grade. In order to reduce the risk of extra expenses from contractor claims resulting from differing sediment conditions, more precise planning was required. Due to the size of the area and the desired accuracy, subbottom profiling was identified as a potential tool to provide high‐density coverage across the site. As with any geophysical tool, ground truth data were required to verify and aid in interpretation. This article describes how subbottom profiling was used to refine the dredge grade for the target layer, the associated challenges related to signal loss in some areas, and how they were overcome. ©2017 Wiley Periodicals, Inc.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".