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Record W7074150775

Fluvial Response to Climate Change in the Pacific Northwest: Skeena River Discharge and Sediment Yield

2023· article· en· W7074150775 on OpenAlexaboutno aff

Bibliographic record

VenuePublication Database GFZ (GFZ German Research Centre for Geosciences) · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPrecipitationSedimentClimate changeHydrology (agriculture)Drainage basinFluvialGlacierSurface runoffDischarge
DOInot available

Abstract

fetched live from OpenAlex

Changes in climate affect the hydrological regime of rivers worldwide and differ with geographic location and basin characteristics. Such changes within a basin are captured in the flux of water and sediment at river mouths, which can impact coastal productivity and development. Here, we model discharge and sediment yield of the Skeena River, a significant river in British Columbia, Canada. We use HydroTrend 3.0, two global climate models (GCMs), and two representative concentration pathways (RCPs) to model changes in fluvial fluxes related to climate change until the end of the century. Contributions of sediment to the river from glaciers decreases throughout the century, while basin-wide overland and instream contributions driven by precipitation increase. Bedload, though increased compared to the period (1981–2010), is on a decreasing trajectory by the end of the century. For overall yield, the model simulations suggest conflicting results, with those GCMs that predict higher increases in precipitation and temperature predicting an increase in total (suspended and bedload) sediment yield by up to 10% in some scenarios, and those predicting more moderate increases predicting a decrease in yield by as much as 20%. The model results highlight the complexity of sediment conveyance in rivers within British Columbia and present the first comprehensive investigation into the sediment fluxes of this understudied river system.

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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.330
Teacher spread0.270 · 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 designNot applicable
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 routes1
Has abstractyes

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