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Record W4298147332 · doi:10.1002/esp.5486

LiDAR‐based semi‐automated mapping of drumlins and mega‐scale glacial lineations of the Green Bay Lobe, Wisconsin, USA: Ice sheet beds as glaciotribological systems

2022· article· en· W4298147332 on OpenAlexafffund
Nick Eyles, Syed Bukhari, Shane Sookhan, Phil Ruscica, R C Paulen

Bibliographic record

VenueEarth Surface Processes and Landforms · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsThe Scarborough HospitalGeological Survey of CanadaUniversity of Toronto
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsDrumlinGeologyLineationBedformGeomorphologyDeglaciationIce sheetIce streamGlacial periodSedimentSediment transportPaleontologyOceanographyCryosphereSea ice

Abstract

fetched live from OpenAlex

Abstract A machine learning methodology for processing and visualizing high‐resolution LiDAR digital data is used to map drumlins and mega‐scale glacial lineations (MSGLs) on the bed of the Late Wisconsin Green Bay Lobe in Wisconsin, USA, which exhibited surge‐like behaviour during deglaciation. Previous work has shown that streamlined bedforms are the product of erosional streamlining of pre‐existing sediment. Analysis of bedform height and elongation ratio using curvature‐based relief separation (CBRS) and K ‐means clustering of 32,003 bedforms reveals a continuum of six morphotypes ranging from drumlins, through “channeled” more elongated multi‐crested drumlins, to MSGLs. Further statistical analysis shows that morphotypes cluster into six types of streamlined surfaces (S1–S6) recording progressive elimination of an antecedent overridden topography to produce a smoother bed. Initial, relatively high‐relief drumlinized surfaces (S1, S2) occur around the slower‐flowing lateral flanks of the lobe, where a pre‐existing hummocky morainal topography was only partially modified by subglacial erosion. More streamlined surfaces (S3, S4) dominated by multi‐crested more elongate drumlins of reduced relief amplitude are transitional to flow sets of MSGL‐dominated surfaces (S5, S6) indicative of much faster‐flowing ice streaming along the lobe's axis. Estimates of basal drag based on roughness calculations for each surface type identify a 61% reduction in frictional retardation from poorly streamlined surfaces S1 and S2 to MSGL‐dominated surfaces S5 and S6, with a step‐like reduction between drumlins and channeled drumlins (S3, S4) possibly recording the rapid onset of fast flow. Subglacial streamlining is argued to be accomplished by a thin (<1 m) “third layer” of deforming subglacial debris between ice and its bed which functioned as an erodent layer. A thin (<3 m) till cap, formed by aggradation of deforming debris, rests unconformably on heterogeneous core sediments. Streamlined subglacial surfaces are comparable to the “functional” surfaces resulting from erosion by a “third layer” of wear debris in engineering tribological systems, and also by gouge on faults. Pleistocene ice sheets expanded over pre‐existing landsystems, pointing to the broader relevance of the methodology and findings reported here.

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)0.001
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 score0.036
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.016
GPT teacher head0.236
Teacher spread0.220 · 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

Citations15
Published2022
Admission routes2
Has abstractyes

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