Bibliographic record
Abstract
The remediation of toxic sediment in harbours and urban waterways requires detailed mapping of contaminated sediment distribution and thickness. Conventional methods rely on interpolation of pollutant concentrations from widely spaced core samples but can lead to significant errors in estimating sediment distribution. An improved approach, as demonstrated by recent work in Hamilton Harbour in Lake Ontario, is to estimate pollutant levels from proxy measurements of sediment magnetic properties. Measurements from 40 core samples collected within the harbour show that the magnetic susceptibility of a contaminated upper layer of sediment is one to two orders of magnitude greater than in the underlying uncontaminated dprecolonialT sediments. The susceptibility contrast results from elevated levels of urban-source magnetic oxides and is sufficient to generate a total field anomaly (ca. 5–40 nT) that can be measured with a towed magnetometer. Systematic lake-based magnetic surveying (N500 line km) of the harbour using an Overhauser marine magnetometer identifies well-defined positive magnetic anomalies that coincide with mapped accumulations of contaminated sediments on the harbour bottom. Forward modelling of the anomalies shows that the magnetic response is consistent with a contaminated upper layer thickness of up to 5 m. Apparent susceptibility maps calculated from magnetic survey data show a close spatial correspondence with core-derived magnetic susceptibilities and provide a rapid means for classifying contaminated sediments. Detection of shallow magnetic anomalies is dependent upon a closely spaced survey grid (b75 m line spacing) and careful post-cruise processing to remove diurnal, regional and water-depth related variations in the magnetic field intensity. D 2004 Elsevier B.V. All rights reserved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 teacher head, 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".