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
Bibliographic record
Abstract
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) andK‐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.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".