Late glacial lake and marine strandlines in the Ontario, St. Lawrence, and Champlain Lowlands, USA and Canada record steadily decreasing water levels interrupted by breakout floods
Bibliographic record
Abstract
Abstract We report new interpretation of >19,500 beach strandlines from waterbodies in the western St. Lawrence and Champlain Lowlands in northern New York and adjacent areas of Vermont, Quebec, and Ontario from ≤2-m-resolution digital elevation models. Strandline evidence supports a deglaciation model in which proglacial lake and marine shoreline deposits adjusted continuously in response to steady shoreline regression linked to outlet incision, differential isostatic adjustments, and postglacial relative sea-level rise. Gaps in strandline preservation reflect times of rapid water-level decline associated with breakout floods and abrupt shifts in drainage to new outlets. Water levels returned to slower, steady decline and renewed beach sedimentation during the later stages of a breakout as water levels in the source and receiving waterbodies equilibrated. Our conclusions contrast with previous models that infer discrete lake stages were controlled by stable outlets then fell abruptly as lower outlets were exhumed from beneath the Laurentide Ice Sheet during deglaciation. We present a new deglacial chronology and lake nomenclature that reflects this paradigm and redefines the spatial and temporal distributions of proglacial lake and marine water in the region.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".