In-situ Weathering of Alluvial Sediments in the Southern Central Andes Recorded by Ground- and Space-Based Hyperspectral Reflectance
Bibliographic record
Abstract
Alluvial fans comprise abandoned sedimentary surfaces undergoing physical and chemical weathering. While weathering pathways and kinetics have been described over seconds to decades, few field-based studies have quantified these processes in alluvial deposits over geologic timespans. We examine 14 alluvial-fan surfaces flanking the Sierra del Aconquija, southern Central Andes, which have ages between 3 and 320 ka as determined by cosmogenic nuclide exposure dating. These fans present an opportunity to study the evolution of alluvial sediments across late-Quaternary timescales; including silicate weathering pathways, products, rates, and sensitivity to known past climate changes. We use space- and ground-based hyperspectral reflectance measurements to characterize surface mineralogy, and we test whether in-situ weathering records signals of landform age and regional climatic history. We collect fan-surface reflectance using both a handheld spectroradiometer and the PRISMA hyperspectral satellite sensor. In both datasets, bridging several orders of magnitude in spatial scale, we detect spectral features indicative of changing quantities of primary minerals, clays, and iron oxides. These patterns suggest a gradual increase in absolute weathering with surface age, but at progressively slower rates over time. Superimposed on the long-term weathering kinetics, secondary minerals are generated in amounts and at rates that correlate systematically with ~23 kyr precessional cycles and millennial-scale climate perturbations. We interpret that these alluvial fans are sensitive archives of past weathering, which was more pronounced during episodes of wetter and warmer climate. Furthermore, the surface signals are corroborated by the downward accumulation of iron oxide, as shown in soil profiles from four alluvial fan units which were spectrally scanned from the surface to below the weathering front. Our findings highlight the geomorphological applications of hyperspectral data for (i) quantifying weathering processes over 1-100 kyr timescales; (ii) developing novel chronometers for alluvial sediments; and (iii) recovering new palaeoclimate signals from terrestrial sedimentary archives.
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 | 0.000 |
| 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) | 0.000 | 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".