Mapping of meltwater pathways around the Keewatin sector of the Laurentide ice sheet from ArcticDEM data
Bibliographic record
Abstract
This dataset contains meltwater pathways i.e. the manual digitisation of all visible traces of subglacial meltwater flow (e.g. meltwater channels, meltwater tracks and eskers). Mapping was undertaken in ArcGIS 10.4 using hill shaded Digital Surface Models (DSMs) following standard practices. Meltwater pathway centrelines were mapped as polylines. We used 10 m ArcticDEM data (freely available at: https://www.pgc.umn.edu/data/arcticdem/). Data is projected to National Snow and Ice Data Centre (NSIDIC) Sea Ice Polar Stereographic North and referenced to WGS84 horizontal datum (EPSG:3413). Mapping was undertaken using the same projection. The meltwater pathways mapped have been updated since the publication of this paper. Updated mapping is included here so as to present the most up to date and complete efforts. However, updated pathways only account for ~ 2 % of total, many of which are smaller, fine-scale features. Most of the updated pathways occur outside of the sample areas used in the paper and thus overall are unlikely to alter any conclusions made within the paper (Lewington et al., 2020). For more details please see the associated paper: Lewington, E.L.M. Livingstone, S.J. Clark, C.D. Sole, A.J. Storrar, R.D. A model for interaction between conduits and surrounding hydraulically connected distributed drainage based on geomorphological evidence from Keewatin, Canada. The Cryosphere. doi: https://doi.org/10.5194/tc-2020-10. 2020
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".