MétaCan
Menu
Back to cohort

Bringing plant-based veterinary vaccines to market: Managing regulatory and commercial hurdles

2015· review· en· W950562869 on OpenAlexafffundabout
Jacqueline MacDonald, Ketan Doshi, Marike Dussault, J. Christopher Hall, Larry A. Holbrook, Ginny Jones, Angelo Kaldis, Cassidy L. Klima, Phil Macdonald, Tim A. McAllister, Michael D. McLean, Andrew Potter, Alex Richman, Heather M. Shearer, Oksana Yarosh, Han Sang Yoo, Edward Topp, Rima Menassa

Bibliographic record

VenueBiotechnology Advances · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTransgenic Plants and Applications
Canadian institutionsUniversity of SaskatchewanUniversity of GuelphGenome PrairieCanadian Food Inspection AgencyAgriculture and Agri-Food Canada
FundersCanadian Food Inspection Agency
KeywordsCommercializationBusinessAnimal healthIntellectual propertyBiotechnologyHuman healthScale (ratio)Production (economics)MarketingVeterinary medicineMedicineEconomicsComputer scienceEnvironmental health

Abstract

fetched live from OpenAlex

The production of recombinant vaccines in plants may help to reduce the burden of veterinary diseases, which cause major economic losses and in some cases can affect human health. While there is abundant research in this area, a knowledge gap exists between the ability to create and evaluate plant-based products in the laboratory, and the ability to take these products on a path to commercialization. The current report, arising from a workshop sponsored by an Organisation for Economic Co-operation and Development (OECD) Co-operative Research Programme, addresses this gap by providing guidance in planning for the commercialization of plant-made vaccines for animal use. It includes relevant information on developing business plans, assessing market opportunities, manufacturing scale-up, financing, protecting and using intellectual property, and regulatory approval with a focus on Canadian regulations.

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)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.031
GPT teacher head0.319
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 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

Citations37
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueBiotechnology AdvancesSame topicTransgenic Plants and ApplicationsFrench-language works237,207