GIS and geomatics for hydrogeodiversity assessment of glaciated mountains: examples from the Western Alps (Italy) and the Coast Mountains (Canada)
Bibliographic record
Abstract
This paper aims at the systematization of knowledge related to geodiversity assessment for water resources and its evaluation within high mountain areas. In this environmental context, geological features, landforms and geomorphological processes, soils and water too are particularly sensitive to climatic and environmental changes, thus giving geodiversity a particularly dynamic character. A multidimensional (regional, local; present, past) approach was developed for analyzing components of geomorphological and hydrogeological systems, both at superficial and underground level, in order to establish a conceptual model and a specific procedure for the evaluation of geodiversity. Spatial and temporal dimensions of glaciated mountain landscapes of the Italian Western Alps (Monte Rosa, Maggiore Lake, Sesia Val Grande UNESCO Global Geopark) and the Coast Mountains of Canada (Mount Meager, Lillohet Valley, Sea-to-Sky Corridor) were mapped and interpreted by means of: 1) detailed interpretation of DEM-derived data, 2) proper selection of Geomatics survey and monitoring tools and 3) targeted application of GIS analytical methods. The selection and processing operations of the elements considered for this evaluation led to the identification of areas characterized by greater values of hydrogeodiversity. Here, the link between surface and underground hydrodynamics becomes closer and intense, thus conditioning the local landscape setting and the interactions of its natural and human components. The conceptual model and related workflow proved to be useful for both a) enhanced accuracy of models of a diversity of geomorphological and hydrogeological elements and processes of mountain regions and b) improved “targeted” knowledge on hydrogeodiversity and increased awareness on related geoheritage. The proposed GIS and Geomatics framework allowed the hydrogeodiversity assessment going well beyond the limit of classical geomorphological and hydrogeological techniques. Difficulty of quantitative analysis over large areas was overcome, and small landscape features and other “hidden” hydrogeological markers could be taken into account. The results of the research strengthened the possibility of strategic management of geological, geomorphological and hydrological heritages within the study areas. In fact, we identified different landscapes and local peculiarities determined by mutual influences between geology and hydrological dynamics and mapped their possible interaction with human activities and infrastructures within areas of enhanced climate change effects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".