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A Comparative Study of Stream‐Gaging Techniques for Low‐Flow Measurements in Two Virginia Tributaries<sup>1</sup>

2009· article· en· W2047693113 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJAWRA Journal of the American Water Resources Association · 2009
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsNickel Institute
FundersU.S. Geological SurveyVirginia Department of Environmental Quality
KeywordsTributaryCurrent (fluid)Current meterSTREAMSNonpoint source pollutionHydrology (agriculture)Environmental scienceMarshAcoustic Doppler current profilerPollutionFlow measurementComputer scienceEngineeringGeologyWetlandEcologyGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract: Nonpoint source pollution (NPS) studies, such as total maximum daily loads development, often require quantification of flow in small first‐order and second‐order streams. Frequently, stream‐gaging techniques are implemented in flows that are below the manufacturer’s recommended minimum velocity. A comparative analysis of the accuracy of current technologies used in NPS pollution stream‐gaging applications and their applicability in low‐flow conditions was conducted. Nine stream‐gaging methods were evaluated for their field and laboratory performance and control structures were used as the statistical control. Analysis of the field investigation data indicated that Marsh McBirney current meter and the One‐orange method were the most accurate in the field while the results of the laboratory experiments found that the Starflow acoustic Doppler and Valeport Braystoke current meter performed best among the 10 methods. Overall, the Marsh McBirney and Valeport Braystoke current meters exhibited the best performance for both field and laboratory situations.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.307
Teacher spread0.287 · 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