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Record W3048496510 · doi:10.1029/2020gl089627

Isostatic Control of Axial Rivers and Large Drainage Basins on Passive Margins

2020· article· en· W3048496510 on OpenAlexafffund
Ruohong Jiao, Jean Braun, Katherine Kravitz

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPassive marginGeologyLithosphereMargin (machine learning)Drainage system (geomorphology)Continental marginDrainage basinFlow (mathematics)Routing (electronic design automation)GeomorphologyDrainageStructural basinPaleontologyTectonicsGeometryRift

Abstract

fetched live from OpenAlex

Abstract More than half of the world's large rivers flow towards the ocean crossing passive continental margins. Here using an analytical solution and numerical models, we demonstrate that on passive margins, river basins may be integrated by major margin‐parallel channels, which form as a flexural isostatic response of the lithosphere to mechanical/erosional unloading along the margin. We analyzed the downstream courses of large rivers flowing across the passive margins and find that the majority of them (31 of 36) have major margin‐parallel channels. Occurrences of these channels are generally consistent with the model predictions, although the exact locations and geometry of these rivers may also be controlled/changed by other factors. Our results suggest that the lithosphere strength has an important control on the geometry of large river systems on passive margins, linking the evolution and routing of the Earth's freshwater systems to its deep interior dynamics.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0000.000
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 head0.028
GPT teacher head0.266
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 source (direct Gemma or distilled Codex), 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

Citations2
Published2020
Admission routes2
Has abstractyes

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Same venueGeophysical Research Letters→Same topicearthquake and tectonic studies→French-language works237,207→