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Record W2901627372 · doi:10.1080/07388551.2018.1489778

Bioreactor-based advances in plant tissue and cell culture: challenges and prospects

2018· review· en· W2901627372 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCritical Reviews in Biotechnology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsUniversity of Guelph
FundersMinisteriet for Fø devarer, Landbrug og Fiskeri
KeywordsBioreactorBiochemical engineeringBioprocessBiotechnologyBiomass (ecology)Process engineeringComputer scienceBiologyEngineeringBotany

Abstract

fetched live from OpenAlex

Bioreactors are engineered systems capable of supporting a biologically active situation for conducting aerobic or anaerobic biochemical processes. Stability, operational ease, improved nutrient uptake capacity, time- and cost-effectiveness, and large quantities of biomass production, make bioreactors suitable alternatives to conventional plant tissue and cell culture (PTCC) methods. Bioreactors are employed in a wide range of plant research, and have evolved over time. Such technological progress, has led to remarkable achievements in the field of PTCC. Since the classification of bioreactors has been extensively reviewed in numerous reviews, the current article avoids repeating the same material. Alternatively, it aims to highlight the principal advances in the bioreactor hardware s used in PTCC rather than classical categorization. Furthermore, our review summarizes the most significant steps as well as current state-of-the-art of PTCC carried out in various types of bioreactor.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.000
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 head0.037
GPT teacher head0.335
Teacher spread0.298 · 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