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Record W4403686305 · doi:10.26034/lu.akwi.2023.4557

Vergleich und Evaluierung verschiedener Clustering Algorithmen und Methoden zur Anwendung auf Wetterdaten zum Definieren von Wetterereignisprofilen und deren Charakteristiken

2023· article· de· W4403686305 on OpenAlexaboutno aff
Julian Erath

Bibliographic record

VenueAnwendungen und Konzepte der Wirtschaftsinformatik · 2023
Typearticle
Languagede
FieldArts and Humanities
TopicSports Science and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

Clusteranalysen mit den Algorithmen KMeans, HAC, GMM & DBSCAN auf Wetterdaten aus Ontario, Kanada, mithilfe von DSR. Ziel ist die Identifizierung von Wetterereignisprofilen. Die entwickelten Profile könnten in Wettervorhersagen, Dashboards und zur Anomaliedetektion Anwendung finden. IBM Deutschland GmbH stellt sieben Jahre historische Wetterdaten bereit, die Potenzial für zukünftige Forschung bieten.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.024
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.032
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0100.008
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.054
GPT teacher head0.322
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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