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Record W4413336061 · doi:10.1017/qua.2025.15

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

2025· article· en· W4413336061 on OpenAlexaboutno aff
David A. Franzi, Brian S. Carl, Hadar S. Pepperstone

Bibliographic record

VenueQuaternary Research · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsBreakoutGeologyGlacial periodOceanographyPhysical geographyArchaeologyHydrology (agriculture)GeomorphologyGeography

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.037
GPT teacher head0.293
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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