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Record W1939312701 · doi:10.1029/2008wr007546

Suspended sediment and carbonate transport in the Yukon River Basin, Alaska: Fluxes and potential future responses to climate change

2009· article· en· W1939312701 on OpenAlexaboutno aff
M. Dornblaser, Robert G. Striegl

Bibliographic record

VenueWater Resources Research · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersU.S. Geological Survey
KeywordsSedimentHydrology (agriculture)Suspended loadGeologyCarbonateDeposition (geology)Sediment transportDrainage basinButteStructural basinEnvironmental scienceDischargeSpring (device)OceanographyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Loads and yields of suspended sediment and carbonate were measured and modeled at three locations on the Yukon, Tanana, and Porcupine Rivers in Alaska during water years 2001–2005 (1 October 2000 to 30 September 2005). Annual export of suspended sediment and carbonate upstream from the Yukon Delta averaged 68 Mt a −1 and 387 Gg a −1 , respectively, with 50% of the suspended sediment load originating in the Tanana River Basin and 88% of the carbonate load originating in the White River Basin. About half the annual suspended sediment export occurred during spring, and half occurred during summer‐autumn, with very little export in winter. On average, a minimum of 11 Mt a −1 of suspended sediment is deposited in floodplains between Eagle, Alaska, and Pilot Station, Alaska, on an annual basis, mostly in the Yukon Flats. There is about a 27% loss in the carbonate load between Eagle and Yukon River near Stevens Village, with an additional loss of about 29% between Stevens Village and Pilot Station, owing to a combination of deposition and dissolution. Comparison of current and historical suspended sediment loads for Tanana River suggests a possible link between suspended sediment yield and the Pacific decadal oscillation.

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)0.002
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 score0.076
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.053
GPT teacher head0.293
Teacher spread0.240 · 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

Citations56
Published2009
Admission routes1
Has abstractyes

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