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Record W6969596785 · doi:10.5281/zenodo.6860470

Wie sich Business-to-Business-Sharing gezielt unterstützen lässt

2022· article· de· W6969596785 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typearticle
Languagede
FieldBusiness, Management and Accounting
TopicDigital Innovation in Industries
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Quarter (Canadian coin)Order (exchange)Context (archaeology)

Abstract

fetched live from OpenAlex

Sharing verzeichnet seit geraumer Zeit ein grosses Wachstum. Erfolgsbeispiele für das Teilen von Ressourcen zwischen Firmen gibt es jedoch nur wenige. Ein Forschungsprojekt der Fachhochschule Nordwestschweiz und der Hochschule Luzern hat untersucht, wie sich das Sharing zwischen Firmen unterstützen lässt. Es wurden Methoden entwickelt, die das Business-to-Business-Sharing begleiten und fördern.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.009
Science and technology studies0.0080.000
Scholarly communication0.0090.003
Open science0.0030.014
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0660.048

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 head0.053
GPT teacher head0.237
Teacher spread0.184 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicDigital Innovation in IndustriesFrench-language works237,207