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Record W2952464697 · doi:10.1089/ind.2019.29172.qyh

Growing the Bioeconomy: Advances in the Development of Applications for Cellulose Filaments and Nanocrystals

2019· article· en· W2952464697 on OpenAlexafffund
Wadood Y. Hamad, Chuanwei Miao, Stephanie Beck

Bibliographic record

VenueIndustrial Biotechnology · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsFPInnovationsCanadian Forest Service
FundersNatural Resources Canada
KeywordsNanotechnologyInvestment (military)Resource (disambiguation)BusinessMultitudeRenewable resourceRenewable energyCelluloseMaterials scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Forestry-based products have long capitalized on the ability of lignocellulosic materials to form fibers, and new forestry-based functional materials have the potential to compete with other materials not only based on performance, as indicated by the scientific evidence, but also on merits of recyclability and being a renewable resource. In addition to cost-effectiveness and product differentiation, the forestry industry is considering new technologies to healthily grow and secure long-term, respectable return on investment. This brief review offers a sense of the technological advances that have been made in the field of nanomaterials, with a vision to developing functional forestry-based materials that could find enhanced applications in a multitude of industries, for instance, intelligent packaging, cosmetics, paints and coatings, foods, and advanced electronic and photonic materials, to name a few examples.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.302
Teacher spread0.265 · 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 designBench or experimental
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

Citations16
Published2019
Admission routes2
Has abstractyes

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