Coastal products of marine transgression in cold-temperate and high-latitude coastal-plain settings: Gulf of St Lawrence and Beaufort Sea
Bibliographic record
Abstract
Abstract Cold climate exerts a clear influence on the processes of marine transgression in mid- and high-latitude coastal-plain settings, but its signature in the depositional record is much clearer at high latitude. Both cases selected for this study are influenced by the legacy of past glaciation and the pervasive effects of ongoing Holocene marine transgression. Both are affected by sea ice. The high-latitude site lies within the zone of continuous permafrost and the abundance of excess ground ice along the Beaufort coast is the dominant factor distinguishing it from the mid-latitude Gulf of St Lawrence (GSL) setting and standard models of transgressive coasts elsewhere. In the southern GSL, the transgressive unconformity (TU) is at the seabed (or buried by a very thin veneer) across the inner shelf; shoreface sand moves landward, keeping pace with the transgressive front through deposition in barriers, dunes and estuaries. The pace of transgression in the Beaufort Sea is influenced by a number of distinctive periglacial erosion processes, including thermal abrasion and thaw subsidence. Marine transgression across this landscape creates intricate breached-lake estuaries and low sandy barrier beaches with limited dunes, leaving distinctive facies suites and geometry, while seaward sediment transport buries the TU on the inner shelf.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".