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Decoding surface processes from escarpment to watershed: Insights from karst landscapes using cosmogenic nuclides and geochemical data

2025· article· en· W4409817121 on OpenAlexaff
Rogério Uagoda, Lionel Siamé, Jérémie Garnier, Laëtitia Léanni, Dandara Caldeira, Régis Braucher, Rémi Freydier, Patrick Seyler, A.S.T.E.R. Team

Bibliographic record

VenueGeomorphology · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsASTER
FundersCentre National de la Recherche ScientifiqueConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorInstitut de Recherche pour le Développement
KeywordsEscarpmentGeologyKarstCosmogenic nuclideSurface exposure datingWatershedGeomorphologyEarth scienceGeochemistryPaleontologyGlacierMoraine

Abstract

fetched live from OpenAlex

Tropical karst landscapes are marked by complex sediment dynamics resulting from the interplay between geomorphic processes and sediment transport . In the Vermelho Watershed (Brazilian savanna), we use paired cosmogenic nuclides ( 10 Be and 26 Al) to quantify denudation rates, burial durations, and sediment mixing. The spatial distribution of Al Be ratios reveals rapid erosion and prolonged burial on escarpments , in contrast to the more stable conditions of flatter areas, where erosion is subdued, and sediment residence time is longer. Comparisons with Iraquara and Serra das Mesas emphasize the influence of topography and sediment sources on nuclide inventories. Downstream increases in freshly eroded material point to fluvial mixing, while discrepancies between cosmogenic burial and OSL ages (Tarimba Cave) suggest partial burial and pre-depositional histories. The integration of cosmogenic, geochemical, and geomorphic data underscores the complexity of tropical karst systems, where vertical mixing and uneven erosion are key processes. These results contribute to a broader understanding of landscape evolution and offer a comparative basis for interpreting cosmogenic signals in fluvio-karst environments worldwide.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.040
GPT teacher head0.277
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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