A GIS Database of Submarine Glacial Landforms and Sediments on High-Arctic Continental Shelves
Bibliographic record
Abstract
A new digital database compiling glacial landforms and sediments in the High Arctic was created in order to facilitate and underpin new research on palaeo-ice sheets and tidewater glacier dynamics. The database is in a geographic information system (GIS) format and will be available for web download when published. It documents evidence of previous glacial activity as visible on the contemporary seafloor of fjords and continental shelves around all of Svalbard, Greenland, and Alaska, and north of 66°30’ N in Russia, Norway, and Canada. Extensive literature research was conducted to create the database, compiling a large number of glacial landforms at a range of scales, sediment cores, and radiocarbon dates. Glacial landforms included in the database are cross-shelf troughs, trough-mouth fans, grounding-zone wedges, overridden moraines, glacial lineations, drumlins, crag-and-tails, medial moraines, terminal moraines, debris-flow lobes (including glacier-contact fans), recessional moraines, De Geer moraines, crevasse-fill ridges, eskers and submarine channels. Sediment core locations are attributed with a description of the sampled lithofacies and sediment accumulation rates where available. Radiocarbon dates were included when thought to be relevant for constraining the timing of large-scale palaeo-ice dynamics. Outlines of bathymetric datasets published before December 2020 were also mapped to give an overview of previously investigated research areas. The database will aid researchers in the reconstruction of ice dynamics during and since the Last Glacial Maximum and in the interpretation of High-Arctic glacial landform-sediment assemblages. Moreover, apart from providing a comprehensive bibliography on Arctic glacial geomorphological and sedimentological research, it is intended to serve as a basis for future ice sheet modelling of High-Arctic glacier dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".