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Record W2479400511 · doi:10.1139/cjps-2015-0268

Response of cool-season grain legumes to waterlogging at flowering

2016· article· en· W2479400511 on OpenAlexvenueno aff
Silvia Pampana, Alessandro Masoni, Iduna Arduini

Bibliographic record

VenueCanadian Journal of Plant Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyAgronomyWaterlogging (archaeology)LupinusShootSativumVicia fabaGrowing seasonBiomass (ecology)White mustardPisumSowingCropVegetative reproductionAbiotic componentHorticulture

Abstract

fetched live from OpenAlex

Soil flooding and submergence, collectively termed waterlogging, are major abiotic stresses that severely constrain crop growth and productivity in many regions. Cool-season grain legumes can be exposed to submersion both at the vegetative and reproductive stages. Limited research has been carried out on these crops with waterlogging imposed at flowering. We evaluated how waterlogging periods of 0, 5, 10, 15, and 20 d at flowering affected seed yield, biomass of shoots, roots and nodules, and N uptake of faba bean (Vicia faba L. var. minor), pea (Pisum sativum L.), and white lupin (Lupinus albus L.). Faba bean tolerated submersion better than pea and white lupin. Pea and white lupin plants did not survive 10 d of submersion, and after 5 d the seed yield, shoot and root biomass, and N uptake had more than halved. Faba bean survived 20 d of waterlogging, although seed and biomass production and total N uptake were severely reduced. Shoot dry weight and seed yield decreased linearly with the duration of waterlogging, which negatively affected seed more than the vegetative plant part weight. In all three crops waterlogging at flowering led to damage, which could not be recovered during seed filling.

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 score0.508
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.200
Teacher spread0.186 · 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

Citations56
Published2016
Admission routes1
Has abstractyes

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