MétaCan
Menu
Back to cohort
Record W2049015494 · doi:10.2135/cropsci2004.0352

Root Temperature and Aeration Effects on the Protein Profile of Canola Leaves

2005· article· en· W2049015494 on OpenAlexafffund
Jennifer Franklin, Nat N. V. Kav, William Yajima, David M. Reid

Bibliographic record

VenueCrop Science · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCanolaAerationBrassicaShootBiologyHorticultureStarchBotanyAerenchymaAgronomyFood scienceEcology

Abstract

fetched live from OpenAlex

Canola ( Brassica napus L.) is planted in early spring and must survive both low soil temperatures and periods of wet weather. Shoot effects result from both low root temperature and low aeration, but little is known about the interaction between these two environmental factors, particularly with respect to changes in gene products. In this study, canola plants (‘46A65’) were treated in solution culture. Shoot temperatures were maintained at day/night temperatures of 24/18°C, while roots were maintained at an ambient temperature of 24/18°C, or cooled to 10°C. Roots were either aerated, or not aerated to create hypoxic treatments. Plants with roots in cold and hypoxic solution accumulated starch in the root, and had greater reductions in fresh weight and leaf area than those in either cold or hypoxic treatments alone. Twenty‐one changes in protein expression were also found in the cold hypoxic treatment, 17 of which were not found in either cold or hypoxic treatments alone. Gene products up‐regulated in leaves included cytochrome oxidase Subunit I (COX1) in response to hypoxia, elongation factor eEF1 γ chain in plants with cooled roots, and chaperonin 10 when roots were cooled without aeration. Results demonstrate the interaction between multiple stresses on a molecular level, and suggest that flooding under cool soil temperatures will be more detrimental to canola than that which occurs at warmer temperatures.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.236

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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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 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

Citations7
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueCrop ScienceSame topicPlant responses to water stressFrench-language works237,207