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Record W2365495829

Response of rape cultivars with different boron efficiency to boron-magnesium nutrition at seedling stage

2004· article· en· W2365495829 on OpenAlexaff
Yunhua Wang

Bibliographic record

VenuePlant Nutrition and Fertilizing Science · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsCultivarBoronBrassicaSeedlingChlorophyllChemistryAgronomyMagnesiumHorticultureBiology
DOInot available

Abstract

fetched live from OpenAlex

Solution culture to studied the responses of two rape ( Brassica napus ) cultivars with different boron efficiency to boron-magnesium nutrition at seedling period. The results showed that increasing Mg concentration in the solution had no influence on the growth of two cultivars under lower B level. However, B content and B accumulation in plants decreased, and Mg content, Mg accumulation and chlorophyll content increased. Similarly, increasing B concentration under lower Mg level, Mg content, Mg accumulation and chlorophyll content decreased, the decreasing extent in the B-inefficient cultivar was larger than in the B-efficient cultivar, but chlorophyll content in B-efficient cultivar was larger than in the B-inefficient cultivar. When increasing B concentration in the solution under higher Mg level, there was a significant synergism between B and Mg. The effect of the synergism in the B-inefficient cultivar was less than in B-efficient cultivar. To a large extent, chlorophyll content in plant was tighter relative to Mg content, and less relative to B content. Boron-magnesium nutrition had no significant influence on Zn content. Under the low Mg condition, increasing B concentration could enhance Mn content significantly. At two Mg levels, increasing B concentration improved Fe nutrition of the two cultivars, however, Fe content in B-inefficiency cultivar raised significantly.

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 score0.160
Threshold uncertainty score0.441

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 studies0.0010.000
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 head0.014
GPT teacher head0.227
Teacher spread0.212 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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