MODELING DRIFT NOSES, AN UNCOMMON FORM OF DRUMLIN
Bibliographic record
Abstract
Drumlins and related streamlined subglacial features (e.g.flutes, drift drumlins, roches moutonnées, whalebacks, rock-cored drumlins, and crag and tails) have had innumerable papers written on their possible origins.Mentioned briefly by Boulton (1987), drift-nosed drumlins are landforms that have received relatively little attention.Examples of these features can be found on the Belcher Islands in Hudson Bay, Nunavut, Canada; based on this location Boulton (1987) argued that the drift noses occur where a bedrock scarp blocked the passage of the drift mass.We apply the term drift noses to similar features found in Clarks Fork Valley, northwestern Wyoming, and in south-central Sweden.All three locations had ice at least 1 km thick.In Wyoming drift noses composed of lodgment till lie on the stoss sides of resistant granitic outcrops.Many drift noses, long rock-cored drumlins, and crag & tails occur in Sweden where individual rock cores are at the stoss ends, the centers, or the lee ends of the features.We attempted physical modeling of these features using a wooden box (our glacial trough) with water-saturated sediment and a small heated copper obstacle on the bottom.For sediment we used Palouse loess (mostly silt) or Vashon lodgment till (with pebbles removed).A groove formed on the bottom of a block of ice (our glacier) as it was shoved past the "bedrock" obstacle.Although no significant drift noses formed, crag & tails developed downglacier of the obstacle, apparently by water-saturated sediment flowing into the groove on the bottom of the glacier.Our only "drift nose" formed as modeling clay was shoved past the obstacle; this suggests that viscosity and/or cohesion may be significant factors controlling the formation of drift noses.We could not determine what other factors might be important; possibilities include water content of the drift, particle size, ice temperature and velocity, and size and shape of the bedrock obstacle.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".