Cartographic Representation of Glacial Phenomena: Historical and Recent Developments
Bibliographic record
Abstract
With the first systematic exploration of glacial phenomena in the middle of the nineteenth century, glaciology became an independent science. In parallel, glacial structures and processes needed to be visualized cartographically. Such representations always reflect the then current states of the technology of glaciological data collection and of cartography. Already in the last century, well-designed and user-friendly glacier maps with a high information content were published with the help of precise geodetic measurements and well-developed cartographic techniques. The focus was upon statistical illustration of the changes in glacier geometry and by glacial-morphological forming. In the 1950s, glaciologists started to represent dynamic parameters such as glacier fluctuations, mass balance, and ice flow. Furthermore, the topographic information of the printed maps was supplemented by orthophotos and satellite images. Today, in the age of digital cartography, screen representations become more and more important. Using digital photogrammetry and remote sensing, new methods of geophysical sounding, or satellite-based global positioning systems (GPS), large quantities of data can be recorded. Using geographic information systems (GIS), these data can be manipulated, modelled, compiled as digital elevation models (DEM), analysed, and finally visualized interactively as high-quality maps or as perspective views. Future trends point towards comprehensive, interactive glacial information systems with integrated functions for database query, modelling, and visualization.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".