Use of morphometric variables and self-organizing maps to identify clusters of alluvial fans and catchments in the north Peloponnese, Greece
Bibliographic record
Abstract
Abstract We applied a computational method to aid in clustering 41 alluvial fans along the southern coast of the Gulf of Corinth, Greece. The morphology of the fans and their catchments was quantitatively expressed through 12 morphometric parameters estimated using geographical information system techniques and the relationships among the geomorphometric features of the fans and their catchments were examined. Self-organizing maps were used to investigate the clustering tendency of fans based on morphometric variables describing both the fans and their corresponding catchments. The results of unsupervised classification through the self-organizing maps method revealed correlations among the morphometric parameters and five groups of alluvial fans were identified. These groups had a clear physical explanation, showed a preferred geographical distribution and reflected the processes related to the development of the fans. The geographical distribution of the fan catchment groups was partially controlled by variations in the relative tectonic uplift rate, which was the main control on the accommodation space for the development and accretion of the fans. The smaller fans were located in the central part of the study area, where the uplift rates were higher, whereas larger fluvial-dominated fan deltas formed to the east and west of the central group, where the uplift rates were lower.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".