Using<scp>GIS</scp>and streamlined landforms to interpret palaeo‐ice flow in northern Iceland
Bibliographic record
Abstract
The properties of streamlined glacial landforms and palaeo‐flow indicators in the valleys of Viðidalur, Vatnsdalur and Svínadalur in northern Iceland were quantified using spatial analyses. Drumlins and mega‐scale glacial lineations (MSGL) were visually identified using satellite imagery from Google Earth, the National Land Survey of Iceland (NLSI) Map Viewer and Landsat satellites, and using aerial photographs from theNLSI. A semi‐automated technique was developed usingENVIto determine regions in northern Iceland likely to contain streamlined landforms. The outlines of the identified landforms were manually delineated in Google Earth, and all analyses were conducted in ArcGISusing a 20 m digital elevation model (DEM) of Iceland from theNLSI. Smaller features such as flutes, grooves and striations were measured in the field. At least 543 drumlins and 90MSGLwere identified in the three valleys. Average elongation ratios for Viðidalur, Vatnsdalur and Svínadalur are 4.3:1, 5.2:1 and 6.7:1, respectively. The average density of streamlined landforms is 2.34 landforms per 1 km2. Striations and orientation data of the drumlins andMSGLdemonstrate ice flow to the northwest into Húnaflói. Parallel conformity is higher in the valley of Svínadalur (9° standard deviation) than in Viðidalur (12°) and Vatnsdalur (16°). Packing values are generally higher in the centre of each valley. The properties of streamlined landforms in the valleys of Viðidalur, Vatnsdalur and Svínadalur support the presence of palaeo‐ice stream activity on northern Iceland. Palaeo‐ice streams flowed from these regions into Húnaflói, supplying ice to the margin of the Iceland Ice Sheet during the Last Glacial Maximum. These palaeo‐ice streams provide a mechanism for ice centres from the mainland of Iceland to reach the shelf‐slope break.
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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 | 0.002 |
| 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".