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Record W2149476256 · doi:10.1093/jxb/eru236

The challenges of commercializing second-generation transgenic crop traits necessitate the development of international public sector research infrastructure

2014· review· en· W2149476256 on OpenAlexaff
Steven J. Rothstein, Yan Bi, Viktoriya Coneva, Mei Han, Allen G. Good

Bibliographic record

VenueJournal of Experimental Botany · 2014
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsUniversity of AlbertaUniversity of Guelph
Fundersnot available
KeywordsBiotechnologyFirst generationWork (physics)Public sectorBusinessBiologyGenetically modified cropsEnvironmental resource managementNatural resource economicsTransgenePolitical scienceEngineeringEnvironmental scienceEconomicsGeneSociologyGenetics

Abstract

fetched live from OpenAlex

It has been 30 years since the first transformation of a gene into a plant species, and since that time a number of biotechnology products have been developed, with the most important being insect- and herbicide-resistant crops. The development of second-generation products, including nutrient use efficiency and tolerance to important environmental stressors such as drought, has, up to this time, been less successful. This is in part due to the inherent complexities of these traits and in part due to limitations in research infrastructure necessary for public sector researchers to test their best ideas. Here we discuss lessons from previous work in the generation of the first-generation traits, as well as work from our labs and others on identifying genes for nitrogen use efficiency. We then describe some of the issues that have impeded rapid progress in this area. Finally, we propose the type of public sector organization that we feel is necessary to make advances in important second-generation traits such as nitrogen use efficiency.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.990
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.189
GPT teacher head0.363
Teacher spread0.175 · 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 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

Citations18
Published2014
Admission routes1
Has abstractyes

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