Bibliographic record
Abstract
Fieldwork and geomorphological mapping have been, and continue to be, important for the study of surface processes, process–form relationships, feedback mechanisms, system couplings, and landscape evolution. Geomorphological mapping has traditionally been a qualitative activity based upon human interpretation and intuition, but the rapid proliferation of geospatial technologies has transformed geomorphological assessment and mapping into an increasingly quantitative endeavor that is rapidly evolving. Numerous developments in geodesy, remote sensing, geographic information and geophysical technology, and high‐performance computing have revolutionized geomorphological mapping. These developments allow surface biophysical, morphological, and near‐surface spatiotemporal data to be integrated and used to develop maps that portray the complexity of geomorphological systems. As conceptual understanding of the landscape and human analytical reasoning continue to serve as the basis for geomorphological mapping, new methodological approaches are being developed to use analytical reasoning to objectively map individual landforms and the broader landscape. This requires, however, conceptual and mathematical formalization of issues in areas of data collection, representation, semantic modeling, scale, indeterminate boundaries, information extraction, information integration, and geovisualization to ensure that geomorphological mapping becomes fundamental to integrative science. Nevertheless, geographers will play a leading role in developing new geospatial technologies to improve the ability to study process mechanics, rates and regimes, process–form relationships, scale dependencies of surface processes and feedback mechanisms, and polygenetic landscape evolution.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".