Debris-covered glaciers/viscous flow features (VFFs) inventory, texture mapping, and topographic profiles in Phlegra Montes, Mars
Bibliographic record
Abstract
This dataset supports the 2026 study titled "Distribution and Surface Morphology of Debris-Covered Glaciers and the Latitude Dependent Mantle (LDM) in Phlegra Montes, Mars" by C.N. Andres et al (https://doi.org/10.1016/j.icarus.2026.116994). The study examines spatial relationships and surface textures of debris-covered glaciers (viscous flow features or VFFs) and the Latitude Dependent Mantle (LDM) in the Phlegra Montes region on Mars — landforms that provide key insight into recent climate-driven surface modification on Mars. In this context, Lobate Debris Aprons (LDAs) are a subcategory of VFFs, that the study specifically focuses on. The dataset includes manually digitized polygons using high-resolution CTX, HiRISE, and MOLA-HRSC basemaps. All spatial data are projected in the GCS_Mars coordinate system (WKID: 104905), based on the Mars 2000 datum and Mars_2000_IAU_IAG spheroid, with units in decimal degrees. Version 1 of the dataset includes: PhlegraMontes_VFFs_CAndres – VFF Inventory (.shp file; polygon shapefile) PhlegraMontes_LDATextures_CAndres – LDA Textures (.shp file; polygon shapefile) PhlegraMontes_LDAElevProfilePoints_200m – LDA Elevation points (.csv file; point data)
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.011 | 0.009 |
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".