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Record W2889998399 · doi:10.5539/jas.v10n10p66

Cl− More Detrimental Than Na+ in Rice Under Long-Term Saline Conditions

2018· article· en· W2889998399 on OpenAlexvenueno aff
Dao Duy Hanh, Yoshihiko Hirai

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsSalinityShootChemistrySodiumGrain yieldYield (engineering)Dry weightAgronomyHorticultureAnimal scienceBiologyMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Rice is considered sensitive to salinity and hence, improving the salinity tolerance of rice is desired to increase productivity. Most research on salinity tolerance for the growth and grain yield of rice focuses on the toxicity of sodium (Na+) and not chloride (Cl−); the information on the negative effects of Cl− on rice is still limited. To learn the difference in the long-term effects of Na+ and Cl− on the whole plant and grain productivities, three rice varieties differing in salinity tolerance were grown in pots irrigated by water with NaCl and KCl in the same molar concentration. The whole plant dry weight and grain yield in all varieties decreased to the same extent after NaCl and KCl treatments compared to the control during the full heading and maturity stages. However, Na+ content in shoots of all varieties under NaCl treatment were higher at all stages than that under KCl treatment and the control. In the most cases, the Cl− content in plants was similar in the NaCl and KCl treatment groups. There was a negative significant correlation between the relative dry weight and grain yield (treated/control) and the Cl− content in the plants. From these results, it was suggested that plant dry weight and grain yield under long-term salinity conditions was reduced by Cl− toxicity rather than Na+ toxicity.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.288
Teacher spread0.259 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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