Quantitative geomorphological analysis of drumlins in the peterborough drumlin field, ontario, canada
Bibliographic record
Abstract
Drumlins are enigmatic subglacial landforms that have been interpreted to form by a number of processes, including incremental accumulation of till, erosion of previously deposited sediment, catastrophic meltwater floods, and sediment deformation. However, relatively little is known about the controls on drumlin formation, such as spatially variable glacial processes or substrate characteristics, and how these controls may be identified from variations in drumlin morphology within a single drumlin field. This paper explores a computational method that allows identification of drumlins and extraction of their morphological characteristics from existing topographic digital data for a portion of the eterborough drumlin field in ntario, anada. Spatial and non‐spatial analysis of the form and distribution of drumlins across the study area identifies drumlin characteristics such as size, elongation ratio, symmetry and long axis orientation and shows that drumlins are not randomly distributed across the region and their form characteristics have distinct regional trends. Kernel density analysis is used to identify the regional trends in drumlin characteristics. Factors that appear to influence the form and distribution of drumlins in the study area include sediment thickness, length of time beneath the ice, ice velocity and direction of ice movement. The distribution of particularly well developed asymmetric and elongate drumlins coincides with the location of a broad bedrock low and is interpreted to identify the former location of a fast‐flowing ice stream.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".