Lake-Based Magnetic Mapping Of Contaminated Sediments, Hamilton Harbour, Ontario, Canada
Bibliographic record
Abstract
The remediation of toxic sediments in harbours and urban waterways requires detailed<br>mapping of contaminated sediment distribution and thickness. Conventional methods rely on<br>interpolation of pollutant concentrations from widely spaced core samples but can lead to<br>significant errors in estimating sediment distribution. An improved approach, as demonstrated by<br>recent work in Hamilton Harbour, in southern Ontario, is to estimate pollutant levels from<br>‘proxy’ measurements of sediment magnetic properties. Measurements from 40 core samples<br>collected within the harbour show that the magnetic susceptibility of the contaminated upper<br>layer of sediment is up to two orders of magnitude greater than in the underlying uncontaminated<br>‘pre-colonial’ sediments. The susceptibility contrast results from elevated levels of urbansourced<br>magnetic oxides and is sufficient to generate a measurable total field anomaly (ca. 5-40<br>nT) that can be measured with a towed magnetometer. Lake-based magnetic surveying (> 500<br>line km) of the harbour using a towed Overhauser marine magnetometer clearly identifies a<br>number of shallow magnetic anomalies which coincide with known contaminant ‘hot-spots’ and<br>accumulations of urban sediments on the harbour bottom. Detection of shallow sediment<br>magnetic response is dependent upon a closely spaced survey grid (< 75 m line spacing, 1 m inline<br>sampling) and careful post-cruise processing to remove diurnal, regional and water-depth<br>related variations in the magnetic field intensity.
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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) | 0.000 | 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 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".