Middle to Late Pleistocene alluvial surface ages recorded by their spectral reflectance in Patagonia, Argentina.
Bibliographic record
Abstract
Alluvial surfaces like fluvial terraces can act as markers for past changes in sediment and water flux along alluvial rivers. By precisely dating terrace surfaces, we can begin to quantify how sedimentary signals propagate though river channels and better infer the paleo-climatic and tectonic conditions during their formation. However, collecting and processing geochronological samples along larger river systems can be costly and time consuming. Here, we extend alluvial landform age control along the Río Santa Cruz of southern Patagonia by applying a spectral surface characteristic model calibrated from a limited set of field samples and cosmogenic nuclide derived exposure ages. This quantitative method leverages the spectral response of surface weathering to ultimately improve age control of the region's fluvial landforms while reducing the time and cost associated with traditional field dating methods.Ages of alluvial surfaces may correlate with time-dependent geochemical weathering processes, such as clay mineral formation. Although previous surface-weathering studies have mostly focused on surface ages since the last or penultimate glaciation, we analyzed the change in weathering state for southern Patagonian fluvial terraces up to a million years old consisting of quarzitic and granitic source lithologies. We find that multispectral Landsat 8 data show a 20% increase in the band 6 to band 2 ratio with terrace elevation (and inferred age), highlighting the higher reflectance in the shortwave infrared band often associated with clay mineral formation. It is likely that weathering rates in the dry and cold Patagonian environment are slower compared to regions with less stable and warmer climate conditions or lithological sources, where age dependent weathering signals in multispectral data tend to saturate on much shorter time scales. Our new 10Be results from surface cobbles and amalgamated pebbles yield exposure ages roughly between 45 and 1000 ka for these surfaces, and the calibrated spectral model allows us to interpolate ages of additional 9 alluvial surface generations based on 11 dated surfaces in the region.Planned in-situ spectral surface measurements will provide robust ground-truthing to the satellite-based observations and allow for further investigation of the mineral changes driving the age-dependent spectral signal. Furthermore, additional terraces will be dated downstream to provide (1) a better understanding of how the weathering process may differ with downstream distance, and (2) a more reliable correlation of surfaces over long distances (> 100 km), enabling us to reconstruct the details of past climate forcing on alluvial-channel evolution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".