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Record W3213385989 · doi:10.1017/qua.2021.53

Glacial deposits and landforms at the terminus of a Laurentide ice stream, Oneida Lake, New York, from multichannel seismic reflection data

2021· article· en· W3213385989 on OpenAlexaboutno aff
Nicholas J. Zaremba, Christopher A. Scholz

Bibliographic record

VenueQuaternary Research · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsDeglaciationGeologyMeltwaterIce sheetGlacial periodGlacial lakeMoraineGlacierShelf icePleistoceneGeomorphologyBedformWisconsin glaciationPaleontologyIce streamOceanographyCryosphereSea iceSediment

Abstract

fetched live from OpenAlex

Abstract The deglaciation record of the Ontario Lowland and Mohawk Valley of North America is important for constraining the retreat history of the Laurentide Ice Sheet, end-Pleistocene paleoclimate, and ice-sheet processes. The Mohawk Valley was an important meltwater drainage route during the last deglaciation, with the area around modern Oneida Lake acting as a valve for meltwater discharge into the North Atlantic Ocean. The Mohawk Valley was occupied by the Oneida Lobe and Oneida Ice Stream during the last deglacial period. Multichannel seismic reflection data can be used to generate images of preglacial surfaces and internal structures of glacial bedforms and proglacial lake deposits, thus contributing to studies of deglaciation. This paper uses 217 km of offshore multichannel seismic reflection data to image the entire Quaternary section of the Oneida basin. A proglacial lake and paleo-calving margin is interpreted, which likely accelerated the Oneida Ice Stream, resulting in elongated bedforms observed west of the lake. The glacial bedforms identified in this study are buried by proglacial lake deposits, indicating the Oneida basin contains a record of glacial meltwater processes, including a 60-m-thick proglacial interval in eastern Oneida Lake.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.180
GPT teacher head0.340
Teacher spread0.159 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2021
Admission routes1
Has abstractyes

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