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Record W6949801893 · doi:10.5281/zenodo.3248603

eWaterCycle II: an online environment for explorative computational hydrology

2019· article· en· W6949801893 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldPsychology
TopicPhilosophy and Theoretical Science
Canadian institutionsJaneway Children's Health and Rehabilitation Centre
Fundersnot available
KeywordsInstallationTinkerVariety (cybernetics)SoftwareProgram code

Abstract

fetched live from OpenAlex

Whether you study compound hazards that require models from different fields of geoscience, or you just want to compare the river discharge predictions from your model to the predictions from another research groups model: running each others (hydrological) models is often a painstaking process. Recognizing the need for hydrologist to not only have access to the software code of each others models, but also to be able to run these models without the tech-support of the researcher that made the model, we have build the eWaterCycle II platform <strong>The goals for the eWaterCycle II project is to provide the hydrological community with tools that:</strong> <strong>Allow the use of a wide variety of models, written in different programming languages, without having to learn those languages.</strong> <strong>Run models needing large amounts of memory and CPUs.</strong> <strong>Have access to all the relevant datasets from the community (forcing, observations)</strong> <strong>Allow advanced use cases such as data assimilation and model coupling studies.</strong> <strong>Allow the sharing of models with the entire community, both for citing (DOIs) and re-use.</strong> <strong>Ultimately providing hydrologists with a toolset that allows them to run each other models, but also adept, couple, and in general tinker with models without the headache of having to delve into each others detailed code.</strong> <strong>Currently one year into this three year project, at the General Assembly we will demonstrate, and make accessible to fellow hydrologist the first version of our system where scientists can:</strong> <strong>Get started with modelling without installing a single piece of software.</strong> <strong>Run any of the available models within minutes.</strong> <strong>Add their own model with minimal effort.</strong> <strong>Compare output of their model, as well as that of colleagues to standard observations like discharge from the Global River Data Centre.</strong> <strong>Develop code quickly in a notebook environment.</strong> <strong>We will demonstrate (and make available to the community) the system we have built during the presentation.</strong>

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.990

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0290.010

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 head0.070
GPT teacher head0.289
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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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
Published2019
Admission routes1
Has abstractyes

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