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Record W2809636032 · doi:10.1139/cjss-2018-0029

Effects of rice (<i>Oryza sativa</i> L.) cultivation and soil type on potassium mobilization and transformation behavior

2018· article· en· W2809636032 on OpenAlexvenueno aff
Di Zhu, Liping Zhan, Rihuan Cong, Xinru Li

Bibliographic record

VenueCanadian Journal of Soil Science · 2018
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsPotassiumSoil waterFertilizerOryza sativaChemistryAgronomySoil typeAnimal scienceEnvironmental scienceSoil scienceBiology

Abstract

fetched live from OpenAlex

To understand the chemical behavior of potassium (K) in soil, rhizobox experiments were conducted to study the effects of K uptake by cultivated rice and soil type on K migration and transformation in soils. The aim of this study was to guide reasonable application of K fertilizer in different soil types. The results showed that at the maximum tillering stage, the migration distances of water-soluble K (Sol-K) were 6 and 5 cm, the depletion of exchangeable K (Ex-K) was 7 and 4 cm, and depletion of nonexchangeable K (Nonex-K) was 1 and 5 cm, respectively, in yellow cinnamon soil (YCS) and fluvo-aquic soil (FS). With the growth of rice, the migration distances of Sol-K showed little difference between YCS and FS. Throughout the season, the contributions of Sol-K, Ex-K, and Nonex-K to K uptake in YCS were 12.0%, 40.0%, and 48.0%, respectively, whereas their contributions in FS were 25.7%, 25.8%, and 48.5%, respectively. K uptake by rice was linearly related to the concentration of different forms of K in soils (R 2 = 0.687*). In conclusion, soil type significantly affected K mobilization and transformation behavior. This indicated that the location of K fertilizer addition in the root zone should differ with soil type.

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 score0.205
Threshold uncertainty score0.738

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 studies0.0000.001
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 head0.013
GPT teacher head0.256
Teacher spread0.244 · 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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