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Record W4321490235 · doi:10.5194/egusphere-egu23-3635

Drivers of inner gorge incision in the Fraser Canyon (British Columbia, Canada)

2023· preprint· en· W4321490235 on OpenAlexaffabout
Erin G. Seagren, Aaron T. Steelquist, Julia Carr, Elizabeth Dingle, Jeff Larimer, Morgan Wright, Derek Heathfield, Isaac J. Larsen, Brian Menounos, Jeremy G. Venditti

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsTula FoundationUniversity of Northern British ColumbiaSimon Fraser University
Fundersnot available
KeywordsBedrockGeologyFluvialGlacial periodSubaerialCanyonDeglaciationErosionGeomorphologyMeltwaterPaleontologyStructural basin

Abstract

fetched live from OpenAlex

Bedrock inner gorges, or narrow and deeply-incised canyons set within broader valleys, are common features in post-glacial landscapes and may reflect the interaction of glacial-fluvial processes. Though widespread, the origins of bedrock inner gorges are enigmatic and have been variably attributed to subglacial meltwater during deglaciation, outburst floods, and subaerial fluvial incision as a response to base level change. It is also unclear if their morphology reflects erosion from a single deglacial period or evolution over multiple glacial-interglacial cycles. Given widespread inner gorges, quartz-bearing rocks, and a history of multiple glaciations, the Fraser Canyon – a 375-km stretch of the Fraser River in British Columbia (Canada) characterized by alternating bedrock and non-bedrock reaches – is an ideal area to explore the drivers of inner gorge incision. Using topographic analyses to characterize the morphology of these bedrock gorges, we assess incision rates required to form the canyons since deglaciation (~14 – 11.7 ka). Using the morphology (e.g., slope) of glacio-fluvial terraces and channel long profile analyses (e.g., ksn), we evaluate whether inner gorges likely formed through 1) interaction of glacial and fluvial erosion during glaciation, 2) one or more catastrophic outburst floods, or 3) steady subaerial fluvial erosion due to isostatic uplift/base level fall since deglaciation. We conclude by exploring the relative efficacy of these erosional processes and implications for the longevity of fluvial and glacial landscapes.

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)0.001
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 score0.012
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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