Inherited clast dispersal patterns: Implications for palaeoglaciology of the <scp>SE K</scp>eewatin <scp>S</scp>ector of the <scp>L</scp>aurentide <scp>I</scp>ce <scp>S</scp>heet
Bibliographic record
Abstract
The net effect of ice‐flow shifts resulting in the dilution or reworking of clasts on a single preserved till sheet is often unknown yet has major implications for palaeoglaciology and mineral exploration. Herein, we analyse variations in till clast lithologies from a single till sheet, within palimpsest‐type Glacial Terrain Zones in NE Manitoba, Canada, to better understand sediment–landform relationships in this area of high landform inheritance. This near‐ice‐divide area is known to consist of a highly fragmented subglacial landscape, resulting from spatio‐temporal variations in intensity of reworking and inheritance throughout multiple glacial events (subglacial bed mosaic). We show that a seemingly homogenous ‘Keewatin’ till sheet is composed of local (>15 km) and continental‐scale (∼100‐km‐long carbonate train and 350–600 km long Dubawnt red erratic train) fan, irregular (amoeboid) or lobate palimpsest dispersal patterns. Local dispersal is more complex than the preserved local landform flowset(s) record, but appears consistent with the overall glacial history reconstructed from regional flowset and striation analyses. The resultant surface till is a spatial mosaic interpreted to reflect variable intensities in modification (overprinting) and preservation (inheritance) of a predominately pre‐existing till sheet. A multi‐faceted approach integrating till composition, regional landforms, ice‐flow indicators, and stratigraphic knowledge is used to map relative spatio‐temporal erosion/reworking intensity.
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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 | 0.001 |
| 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".