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Record W4311083508 · doi:10.1049/enb2.12026

The roadmap of bioeconomy in China

2022· review· en· W4311083508 on OpenAlexaff
Xu Zhang, Cuihuan Zhao, Ming‐Wei Shao, Yi‐Ling Chen, Puyuan Liu, Guo‐Qiang Chen

Bibliographic record

VenueEngineering Biology · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicBioeconomy and Sustainability Development
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsChinaGeographyRegional scienceArchaeology

Abstract

fetched live from OpenAlex

The bioeconomy drives the development of life science and biotechnology as a blueprint for the future development of human society, and offers a cross-cutting perspective on the societal transformation towards long-term sustainability and the transition away from the non-renewable economy. Moreover, the sustainable bioeconomy strategies are consistent with the United Nation's (UN) Sustainable Development Goals (SDG) and are becoming the centre of the achievement for SDG. The Chinese '14th Five-Year Plan for Bioeconomy Development' (2021-2025), including the development goals of China's bioeconomy containing biomedicine, agriculture, bio-manufacturing and bio-security as a strategic priority, is discussed. The plan offers three pathways to improve bioeconomy, including technological innovation, industrialisation and policy supports. Finally, it concludes China's first bioeconomy development plan as a success, suggesting the key role of industrial biotechnology in bioeconomy.

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)0.001
metaresearch head score (Gemma)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.241
Teacher spread0.221 · 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
GenreReview

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

Citations44
Published2022
Admission routes1
Has abstractyes

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