Virtual field sites at the UNESCO Geopark Serra da Estrela (Portugal)
Bibliographic record
Abstract
Virtual field sites are increasing in popularity beyond classrooms. Also, field investigations greatly benefit from DGPS-referenced 3D models. We show a selection of field sites at the UNESCO Geopark Serra da Estrela (Portugal) and how they were implemented into field research. In detail, the advantages of high-definition virtual models of tors, i.e., large residual vertical rocks that are still enrooted in bedrock, were explored in the modeling of the recently conceived tor exhumation approach (TEA; Raab et al., 2018) that can trace surface degradation over a multimillennial period within the landscape of interest. The TEA uses in-situ terrestrial cosmogenic nuclides (TCNs) to surface exposure date (SED) along the surfaces of tors.Animated tors of two endmember sites, formerly glaciated and non-glaciated, contributed to derived continuous surface denudation variations covering the last ~200 ka and revealed glacial retreat dynamics in good detail. Higher isotope contents, thus, surface exposure ages were found in the non-glaciated area. In the formerly glaciated site, the ice margin distance correlates with the isotope signature of the tors. Higher elevation tors have experienced mass wasting and water erosion during the transition from a cool, moist, oceanic-Mediterranean climate. There, the younger rock surfaces have a higher chemical weathering degree than older surfaces at the non-glaciated site.In summary, we provide freely available 3D field sites coupled with an extensive geomorphological study of these landscape features.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".