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Record W2809549853 · doi:10.1111/1751-7915.13285

<i>Fifty Percent Human</i> – how art brings us in touch with our microbial cohabitants

2018· editorial· en· W2809549853 on OpenAlexaff
Sonja Bäumel, Hanne L. P. Tytgat, Birgit Nemec, Ruth Schmidt, Loo Wee Chia, Hauke Smidt

Bibliographic record

VenueMicrobial Biotechnology · 2018
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
FundersWageningen University and ResearchEidgenössische Technische Hochschule ZürichStimuleringsfonds Creatieve IndustrieEuropean Commission
KeywordsHuman Microbiome ProjectMultidisciplinary approachMicrobiomeHuman healthThe artsPoliticsOrder (exchange)Human bodyHuman microbiomeEnvironmental ethicsBridge (graph theory)Field (mathematics)Engineering ethicsSociologyPolitical scienceVisual artsSocial scienceBiologyArtEngineeringBusinessMedicineLawBioinformaticsPhilosophy

Abstract

fetched live from OpenAlex

The Human Microbiome, as well as the exploration of the microorganisms inhabiting the human body, are not only integral to the field of microbiology but represent an intrinsic part of all human beings. Consequently, along with scientists, artists have been inspired by the microbiome: transforming it in to tangible artefacts in order to critically question, reflect on and break down the barrier between humans and their microcohabitants. By artistic means, artists help us to understand how microbial research topics are inevitably affected by societal influences, including (health) politics, economics and the arts. Fifty Percent Human is a multidisciplinary artistic research project that aims to reshape our understanding of the human body and its environment as well as to explore possibilities for conscious coexistence in order to bridge the gap between science and society.

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)0.003
metaresearch head score (Gemma)0.014
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0030.004
Scholarly communication0.0080.005
Open science0.0020.002
Research integrity0.0110.016
Insufficient payload (model declined to judge)0.0130.009

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 head0.003
GPT teacher head0.231
Teacher spread0.228 · 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
GenreEditorial

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
Published2018
Admission routes1
Has abstractyes

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