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Record W4393622800 · doi:10.5281/zenodo.6007229

Rahmendaten für die Energiesystemmodellierung Deutschlands

2022· dataset· de· W4393622800 on OpenAlexaboutno aff
Felix Kullmann, Matteo Giesen, Peter Markewitz, Leander Kotzur, Detlef Stolten

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languagede
FieldEnergy
TopicRenewable Energy and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsDie (integrated circuit)Computer scienceOperating system

Abstract

fetched live from OpenAlex

Dieser Datensatz beinhaltet Rahmendaten, die in aktuellen Studien und Energiesystemmodellen verwendet wurden, um Szenarien für das deutsche Energiesystem zu erstellen. Folgende Rahmendaten sind inklusive ihrer Änderung im Betrachtungszeitraum der jeweiligen Studie erfasst: Energiepreise, CO2-Preise, Wirtschaftswachstum, Bruttoinlandsprodukt, Bevölkerung, Erwerbstätige, Haushalte, Wohnfläche, Nutzfläche, Anzahl PKW, Verkehrsleistung, Industrieproduktion. Diese Arbeit wurde finanziert durch das Bundesministerium für Wirtschaft und Energie im Rahmen des Projekts METIS (Projektnummer 03ET4064A).

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.043
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0430.056

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 head0.025
GPT teacher head0.256
Teacher spread0.230 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicRenewable Energy and SustainabilityFrench-language works237,207