MétaCan
Menu
Back to cohort
Record W6931723689 · doi:10.5683/sp3/iidw9n

Low Red to Far-Red Light Environment Alters Nitrogen Assimilation in Corn (Zea mays)

2022· dataset· en· W6931723689 on OpenAlexaff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNitrogen assimilationAssimilation (phonology)NitrogenPhytochromeNitrateArabidopsisNitrogen cycle

Abstract

fetched live from OpenAlex

In the absence of direct resource competition, far-red light reflected from neighbouring weeds compromises light quality (red to far-red ratio; R/FR) which inactivates phytochrome (Phy) resulting in the regulation of various physiological processes. The main objective of this research was to investigate the effects of low R/FR light on nitrogen assimilation in corn. To explore this, changes to the nitrogen assimilation pathway were measured in corn seedlings nine days post-emergence under low R/FR and control light conditions. The observed results indicate that nitrate levels increased and ferredoxin-dependent glutamine:2-oxoglutarate aminotransferase activities decreased under low R/FR light, however, no other pathway enzymes were affected. Changes in the pathway appear to be PhyB-independent, as Arabidopsis phyB mutant did not change the nitrate levels compared to wild-type Arabidopsis. The research indicates the importance of mitigating early-season weed competition and offers insight into the early physiological mechanisms involved in nitrogen utilization under resource-independent competition. This data was collected as part of William Kramer's MSc research work (see Related Publication).

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)0.002
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: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.005

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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Atrium (University of Guelph)Same topicProtein Degradation and InhibitorsFrench-language works237,207