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Record W4387573151 · doi:10.18174/638736

Maatschappelijke impact van eiwittransitiet

2023· report· nl· W4387573151 on OpenAlexaff
W.H.M. Baltussen, Sander Biesbroek, Sophie Galema, Bas Janssens, Myrna van Leeuwen, Behrang Manouchehrabadi, M.J.G. Meeusen, E.B. Oosterkamp, Jonna Snoek

Bibliographic record

Venuenot available
Typereport
Languagenl
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

In dit onderzoek is de maatschappelijke impact van eiwittransitie vastgesteld.Daartoe is het effect van al ingezet beleid voor 2030 bepaald waarna scenario's waarin meer plantaardig eiwit wordt geconsumeerd (60%) zijn vergeleken met dit basisscenario in 2030.Het gaat om de impact op natuurlijk, sociaal en humaan kapitaal.Ook voor een scenario met verlaagde totale eiwitconsumptie is de maatschappelijke impact beoordeeld.Conclusie is dat tot 2030 vooral de milieu-impact gaat verminderen door forse verkleining van de veestapel.De eiwittransitie heeft een positieve impact op natuurlijk en humaan kapitaal.

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)0.005
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.081
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0070.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0810.009

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 head0.066
GPT teacher head0.378
Teacher spread0.312 · 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
GenreOther

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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