The Field Geomorphologist in a Time of Artificial Intelligence and Machine Learning
Bibliographic record
Abstract
An increasing number of papers incorporate machine learning (ML) approaches to analyze spatially and temporally rich data sets in geomorphology. These data-driven approaches have the potential to significantly improve our understanding of complex systems across a range of scales and support the development of new theories of landform and landscape development that can eventually be incorporated into predictive models. Coupled with the growing availability of remotely sensed data, geomorphology could move further toward a desk-based science and erosion of the field tradition. Using examples from coastal geomorphology, this review of ML applications argues that the development of models that are scalable and can be translated between sites is dependent on experience in the field. Although ML models are shown to be effective as a surrogate to process-based numerical models, they are only as good as our conceptual understanding of landform and landscape form and evolution. This means that ML is simply a new and powerful tool in the proverbial belt of the geomorphologist and should not come at the expense of the field tradition that informs us of whether ML results are accurate, transferable, and scalable.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".