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Record W2943918481 · doi:10.1029/2018jf004885

Base Level and Lithologic Control of Drainage Reorganization in the Sierra de las Planchadas, NW Argentina

2019· article· en· W2943918481 on OpenAlexafffund
Erin G. Seagren, Lindsay M. Schoenbohm

Bibliographic record

VenueJournal of Geophysical Research Earth Surface · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsForeland basinGeologyDrainageDrainage basinTributaryLithologyGeomorphologyHydrology (agriculture)Structural basinPaleontologyGeographyCartography

Abstract

fetched live from OpenAlex

Abstract Planform drainage reorganization in catchments is a common response to changes in boundary conditions. Rivers reorganize through two predominant mechanisms: discrete stream capture events and progressive divide migration. Using general geomorphic observations, flow azimuth analysis, χ anomalies, local headwater relief, and the normalized steepness index ( k sn ), we examine drainage reorganization, its potential controls, and the response timescales for drainages in the Sierra de las Planchadas, NW Argentina. We additionally expand on the comparison of trunk and tributary k sn values as a potential tool for recognizing drainage reorganization. We identify three significant patterns of reorganization: (1) the migration of the main drainage divide (MDD) toward the hinterland, (2) capture of longitudinal drainages by transverse reaches, and (3) shrinking foreland catchments that will result in changes to basin outlet spacing. We determine that the migration of the MDD is due to local base level differences between the major range‐bounding basins, while more local patterns, such as the capture of longitudinal drainages, are the result of the distribution of erosionally resistant lithologies. We additionally assess the response timescales required for topologic changes to the drainage network, such as divide migration; we conclude that, as suggested by modeling studies, they are longer than the timescale required for channel profile adjustment.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.286
Teacher spread0.257 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations33
Published2019
Admission routes2
Has abstractyes

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