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Record W4387267585 · doi:10.1021/acssuschemeng.3c04873

Recent Advances of Biodegradable Agricultural Mulches from Renewable Resources

2023· article· en· W4387267585 on OpenAlexafffund
Yu Li, Chao Liu, Haiying Wei, Xiaoqian Gai, Tong Lei, Yu‐Ting Wang, Qiang Li, Huining Xiao

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of New Brunswick
FundersState Key Laboratory of Pulp and Paper EngineeringNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaNanjing Forestry University
KeywordsMulchRenewable resourceAgricultureRenewable energySustainable agricultureNatural resource economicsEnvironmental pollutionEnvironmental scienceBusinessEnvironmental protectionEngineeringEconomicsAgronomyEcology

Abstract

fetched live from OpenAlex

The extensive utilization of nonbiodegradable plastic agricultural mulch in the past decades has resulted in severe environmental pollution and soil fertility decline. Biodegradable mulch film (BMF) made from renewable resources has been considered an ideal alternative to traditional plastic mulch, offering a sustainable solution to address the challenges of plastic mulch recycling and the associated environmental contamination. More importantly, BMF is compostable, which has garnered significant attention in the realm of sustainable agriculture. This review summarizes recent advances in the application of renewable resources, including polysaccharide, protein, agricultural and forestry waste, and other biological feedstocks, for preparing BMF. Also, the physical characteristics and economic cost of BMF are discussed in detail. Lastly, current issues and future prospects are proposed for sustainable and cost-effective BMF.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.231
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations22
Published2023
Admission routes2
Has abstractyes

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