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Record W4224281163 · doi:10.1029/2021jf006236

Quantifying Rates of Landscape Unzipping

2022· article· en· W4224281163 on OpenAlexaff
Emma Harrison, Brandon McElroy, Jane K. Willenbring

Bibliographic record

VenueJournal of Geophysical Research Earth Surface · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsDalhousie University
FundersNational Science Foundation
KeywordsTransectGeologyCosmogenic nuclideCanyonHead (geology)Range (aeronautics)InterglacialPhysical geographyMagnitude (astronomy)Glacial periodGeomorphologyHydrology (agriculture)OceanographyGeography

Abstract

fetched live from OpenAlex

Abstract Measuring rates of valley head migration and determining the timing of canyon‐opening are insightful for the evolution of planetary surfaces. Spatial gradients of in situ‐produced cosmogenic nuclide concentrations along horizontal transects provide a framework for assessing the migration of valley networks and similar topographic features. We developed a new derivation for valley head retreat rates from the concentrations of in situ‐produced cosmogenic radionuclides in valley walls. The retreat rate is inversely proportional to the magnitude of the spatial concentration gradient and proportional to local nuclide production rates. By solving for a spatial gradient in concentration along a valley parallel transect, we created an expression for the explicit determination of valley head retreat, which we refer to herein as unzipping. We applied this expression to a seepage‐derived drainage network developing along the Apalachicola River, Florida, USA. Sample concentrations along a valley margin transect varied systematically from 2.9 × 10 5 to 3.5 × 10 5 atoms/g resulting in a gradient of 160 atoms/g/m, and from this value a valley head retreat rate of 0.025 m/y was found. The discrepancy between overall network age and current rates of valley head migration suggests intermittent network growth which is consistent with glacial‐interglacial precipitation variations during the Pleistocene. This method can be applied to a wide range of Earth‐surface environments. For the 10 Be system, this method should be sensitive to unzipping rates bounded between 10 −6 and 10 0 m/y.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.116
GPT teacher head0.369
Teacher spread0.253 · 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
Published2022
Admission routes1
Has abstractyes

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