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Record W2322600088 · doi:10.1061/41036(342)488

Developments on Stochastic Analysis, Modeling, and Simulation (SAMS 2009)

2009· article· en· W2322600088 on OpenAlexaff
José D. Salas, Óli Grétar Blöndal Sveinsson, Taesam Lee, William L. Lane, D. K. Frevert

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2009 · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Drought Analysis
Canadian institutionsInstitut National de la Recherche Scientifique
FundersInnovative Research Group Project of the National Natural Science Foundation of China
KeywordsAutoregressive modelComputer scienceNonparametric statisticsStochastic modellingParametric statisticsParametric modelMultivariate statisticsStochastic processSoftwareStochastic simulationEconometricsMachine learningProgramming languageMathematicsStatistics

Abstract

fetched live from OpenAlex

Stochastic Analysis Modeling and Simulation (SAMS) is a software where stochastic techniques are utilized for simulating synthetic water resources data such as monthly streamflows. The current version, SAMS 2007, includes alternative modeling approaches and data analysis features. The stochastic models included in SAMS 2007 are parametric models such as multivariate autoregressive and disaggregation linear models. SAMS 2009 is now available. Various alternatives and options have been added particularly nonparametric techniques. The main purpose of this paper is to summarize the capabilities of SAMS 2009.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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 designSimulation or modeling
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

Citations5
Published2009
Admission routes1
Has abstractyes

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