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Record W1504813672 · doi:10.1002/0470869143.kc043

Perennial Plants as a Production System for Pharmaceuticals

2004· other· en· W1504813672 on OpenAlexaff
Marc‐André D’Aoust, Ursula Busse, Michèle Martel, Patrice Lerouge, Damien Levesque, Louis‐Philippe Vézina

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTransgenic Plants and Applications
Canadian institutionsMedicago (Canada)
Fundersnot available
KeywordsPerennial plantProduction (economics)PopulationTransformation (genetics)BiotechnologyProtein expressionBiologyBiochemical engineeringBotanyEngineeringGeneEconomicsBiochemistry

Abstract

fetched live from OpenAlex

Abstract For molecular farming applications, the capacity to transform perennial plants for recombinant protein production gives access to stable and almost perpetual production of pharmaceuticals from a single population of plants. Being able to produce a bioactive protein with a uniform and well characterised population not only reduces production costs but more importantly, it ensures even and predictable production rate of a uniform product from a genetically stable population. Despite their promising potential, two main hurdles hindered the early development of perennial plant‐based expression systems: the lack of adapted expression cassettes and the difficulty to genetically transform and regenerate the best‐suited perennials. This chapter describes recent scientific breakthrough in alfalfa transformation and protein expression and purification that opened the way to the development of an efficient recombinant protein production platform using alfalfa as 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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.308
Teacher spread0.288 · 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
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

Citations6
Published2004
Admission routes1
Has abstractyes

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