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Record W2184605808 · doi:10.4141/cjps-2014-370

Morphological root responses of soybean to rhizosphere hypoxia reflect waterlogging tolerance

2015· article· en· W2184605808 on OpenAlexvenueno aff
Yutaka Jitsuyama

Bibliographic record

VenueCanadian Journal of Plant Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceHokkaido University
KeywordsRhizosphereWaterlogging (archaeology)Dry weightBiologyHypoxia (environmental)CultivarAgronomyDNS root zoneHorticultureBotanyOxygenChemistryEcology

Abstract

fetched live from OpenAlex

Jitsuyama, Y. 2015. Morphological root responses of soybean to rhizosphere hypoxia reflect waterlogging tolerance. Can. J. Plant Sci. 95: 999–1005. Excess soil moisture induces hypoxia, causing waterlogging injury in soybeans [Glycine max (L.) Merr.]. Twelve Japanese soybean cultivars with varying hypoxia tolerance were used. Of these, 11 (all but Hayahikari) were evaluated for waterlogging tolerance using a scaled index with data from previous studies. To investigate hypoxic responses, cultivars were grown under hydroponic conditions for 2 wk a year for 2 yr, with aerobic or hypoxic oxygen concentrations artificially maintained in the rhizosphere. Hypoxic responses (measured as plant dry weight and root morphology) were assessed at the early vegetative stage. The effects of hypoxic treatment on root dry weight were significant, and the effect of year on soybean dry weight was not significant. The change in root dry weight, and particularly, in coarse root length, was significantly correlated with waterlogging tolerance index at the 0.001 probability level. This study showed that root responses to rhizosphere hypoxia might reflect waterlogging tolerance in soybeans.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.044
GPT teacher head0.241
Teacher spread0.198 · 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

Citations35
Published2015
Admission routes1
Has abstractyes

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