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Record W2158940412 · doi:10.1139/b07-019

Engineering nitrogen use efficiency with alanine aminotransferase

2007· article· en· W2158940412 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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Botany · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsAgriculture Food and Rural DevelopmentUniversity of AlbertaBishop's University
FundersGenome Canada
KeywordsCanolaNitrogenGenetically modified cropsLimitingAgronomyAlanine aminotransferaseBiomass (ecology)ProductivityCropTransgeneBiologyChemistryBiotechnologyBiochemistryEngineeringGene

Abstract

fetched live from OpenAlex

Nitrogen (N) is the most important factor limiting crop productivity worldwide. The ability of plants to acquire N from applied fertilizers is one of the critical steps limiting the efficient use of nitrogen. To improve N use efficiency, genetically modified plants that overexpress alanine aminotransferase (AlaAT) were engineered by introducing a barley AlaAT cDNA driven by a canola root specific promoter (btg26). Compared with wild-type canola, transgenic plants had increased biomass and seed yield both in the laboratory and field under low N conditions, whereas no differences were observed under high N. The transgenics also had increased nitrate influx. These changes resulted in a 40% decrease in the amount of applied nitrogen fertilizer required under field conditions to achieve yields equivalent to wild-type plants.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.169
Teacher spread0.157 · 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