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Record W4323664181 · doi:10.3390/plants12061234

Separate Effects of Sodium on Germination in Saline–Sodic and Alkaline Forms at Different Concentrations

2023· article· en· W4323664181 on OpenAlexafffund
Yasmeen Hitti, Sarah MacPherson, Mark Lefsrud

Bibliographic record

VenuePlants · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGerminationSalinityChemistrySalineSodiumHorticultureSodic soilAgronomyAlkali soilBiology

Abstract

fetched live from OpenAlex

Salinity negatively impacts crop productivity, yet neutral and alkali salt stresses are not often differentiated. To investigate these abiotic stresses separately, saline and alkaline solutions with identical concentrations of sodium (12 mM, 24 mM and 49 mM) were used to compare the seed germination, viability and biomass of four crop species. Commercial buffers containing NaOH were diluted to generate alkaline solutions. The sodic solutions tested contained the neutral salt NaCl. Romaine lettuce, tomato, beet, and radish were seeded and grown hydroponically for 14 days. A rapid germination was observed for alkaline solutions when compared to saline–sodic solutions. The highest plant viability recorded (90.0%) was for the alkaline solution, containing 12 mM Na+, and for the control treatment. Plant viability, with a value of 49 mM Na+ in saline–sodic and alkaline solutions, was the lowest (50.0% and 40.8% respectively), and tomato plants did not germinate. EC values were higher for the saline–sodic solutions than the alkaline solutions, yielding greater fresh mass per plant for all species, with the exception of beets grown in alkaline solution, with a value of 24 mM Na+. The fresh mass of romaine lettuce grown in the 24 mM Na+ saline–sodic solution was significantly greater than romaine lettuce grown in the alkaline solution with the same sodium concentration.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.097

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 head0.015
GPT teacher head0.238
Teacher spread0.224 · 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 designObservational
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
Published2023
Admission routes2
Has abstractyes

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