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Record W1520139924

Comparison of Multi-element Extraction Methods to Determine Available Phosphate and Exchangeable Cations of Korean Soils

2009· article· en· W1520139924 on OpenAlexaboutno aff
김명숙, Yang Jae E, Yoo-Hak Kim, Yoon Jung Hui, Yong-Seon Zhang, Kwak Han Kang, Sang Keun Ha, Byung-Keun Hyun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Soil, Plant Science
Canadian institutionsnot available
Fundersnot available
KeywordsPhosphateExtraction (chemistry)ChemistrySoil waterSoil testAnalytical Chemistry (journal)Environmental chemistryChromatographySoil scienceEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

Soil testing is one of the best management practices for sustainable agriculture. Recently, as increasing soil testing needs, simplification of soil analytical procedure has been required. To determine recommendable multi-element extractant, the soil testing results of available phosphate and exchangeable cations between the conventional methods (Lancaster and 1M ) and multi-element extraction methods such as Mehlich III, Modified Morgan and Kelowna methods were compared. There were highly significant correlation between the conventional methods and multi-element extraction methods (Mehlich III, Modified Morgan and Kelowna) for available phosphate and exchangeable K, Ca, Mg and Na. The coefficients of determination () between available phosphate extracted by Lancaster method and multielement extraction methods were in the order of Mehlich III () > Kelowna () > Modified(Mod.). Morgan (). For exchangeable cations, there were highly significant correlations between 1M method and Mehlich III, Mod. Morgan and Kelowna. However, exchangeable K, Ca and Mg by Mehlich III method were more highly correlated with conventional method than other methods. Therefore, Mehlich III extraction method could be recommended as a single extractant for simultaneous measurement using ICP in the analysis of avaliable phosphate and exchangeable cations.

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 score0.247
Threshold uncertainty score0.172

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 head0.094
GPT teacher head0.357
Teacher spread0.263 · 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
Published2009
Admission routes1
Has abstractyes

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Same topicAgriculture, Soil, Plant ScienceFrench-language works237,207