A map of large Canadian eskers from Landsat satellite imagery
Bibliographic record
Abstract
Meltwater drainage systems beneath ice sheets are a poorly understood, yet fundamentally important environment for understanding glacier dynamics, which are strongly influenced by the nature and quantity of meltwater entering the subglacial system. Contemporary sub-ice sheet meltwater drainage systems are notoriously difficult to study, but we can utilise exposed beds of palaeo-ice sheets to further our understanding of subglacial drainage. In particular, eskers record deposition in glacial drainage channels and are widespread on the exposed beds of former ice sheets. This paper presents a 1:5,000,000 scale map of >20,000 large eskers (typically > 2 km long) deposited by the Laurentide Ice Sheet (LIS), mapped from Landsat imagery of Canada, in order to establish a dataset suitable for analysis of esker morphometry and drainage patterns at the ice sheet scale. Comparisons between eskers mapped from Landsat imagery and aerial photographs indicate that, in most areas, approximately 75% of eskers are detected using Landsat. The data presented in this map build on and extend previous work in providing a consistent map of an unprecedented sample of eskers for quantitative analysis. It offers an alternative perspective on the problems surrounding ice-sheet meltwater drainage and can be used for: (i) detailed investigations of esker morphometry and distribution from a large sample size; (ii), testing of numerical models of meltwater drainage routing that predict esker characteristics (e.g. channel spacing, sinuosity), (iii) assessment of the factors that control esker location and formation; and (iv), a refined understanding of ice margin configurations during retreat of the LIS.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.001 |
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".