Ground‐penetrating radar study of Pleistocene ice scours on a glaciolacustrine sequence boundary
Bibliographic record
Abstract
Ice scouring of lake and sea‐floor substrates by the keels of drifting ice masses is a common geological process in modern northern lakes and continental shelves, and was widespread during the Pleistocene. Nonetheless, the importance of scouring as a geological process is not yet matched by many sedimentological studies of scour structures exposed in outcrop. This article presents an integrated study combining outcrop sedimentology and subsurface ground‐penetrating radar (GPR) data from a relict late Pleistocene ice‐scoured glacial lake floor now preserved below beach sediments in Ontario, Canada. Scours occur along a regressive sequence boundary where deep‐water muddy facies are abruptly overlain by shallow‐water sands resulting from an abrupt drop in water levels. This has allowed the keels of drifting ice masses to scour into muds. Three‐dimensional data gained from the GPR survey show that scours are as much as 2.5 m deep and 7 m wide; they have berms of displaced sediment and are oriented parallel to the former shoreline. Scoured shoreface sediments that fill scours show abundant liquefaction structures, indicating substrate dewatering during repeated scouring events similar to that recently reported in the modern Beaufort Sea in Canada's far north. Marked changes in water depths are typical of glacially influenced lakes and seas, creating opportunities for drifting ice to scour into offshore muddy cohesive facies and be preserved. The data presented here may aid identification in ancient successions elsewhere.
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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 | 0.000 | 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 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".