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Record W2942507049 · doi:10.3133/fs20193024

Remote sensing of river flow in Alaska—New technology to improve safety and expand coverage of USGS streamgaging

2019· article· en· W2942507049 on OpenAlexaboutno aff
Jeffrey S. Conaway, Jack Eggleston, Carl J. Legleiter, John W. Jones, Paul J. Kinzel, John W. Fulton

Bibliographic record

VenueFact sheet · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsHydropowerStreamflowGeological surveyFloodplainHydrology (agriculture)RecreationResource (disambiguation)Water resourcesWater resource managementEnvironmental scienceSTREAMSGeographyEnvironmental resource managementGeologyDrainage basinCartographyEngineering

Abstract

fetched live from OpenAlex

The U.S. Geological Survey (USGS) monitors water level (water surface elevation relative to an arbitrary datum) and measures streamflow in Alaska rivers to compute and compile river flow records for use by water resource planners, engineers, and land managers to design infrastructure, manage floodplains, and protect life, property, and aquatic resources. Alaska has over 800,000 miles of rivers including the Yukon River, the third longest river in the United States. These rivers are home to rare and important ecosystems and are used for recreation, hydropower generation, commercial fishing, and transportation. River flow measurements are essential for wise and safe development and use of Alaska rivers.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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