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Record W2202576697

토양의 유효태 인산과 치환성 양이온의 다성분동시추출 분석방법 비교

2009· article· ko· W2202576697 on OpenAlexaboutno aff
김명숙, 양재의, 김유학, 윤정희, 장용선, 곽한강, 하상건, 현병근

Bibliographic record

Venue한국토양비료학회지 · 2009
Typearticle
Languageko
FieldAgricultural and Biological Sciences
TopicAgriculture, Soil, Plant Science
Canadian institutionsnot available
Fundersnot available
KeywordsPhosphateChemistryExtraction (chemistry)Soil testSoil waterAnalytical Chemistry (journal)ChromatographySoil 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 method(Lancasterand1MNH 4 OA c ) 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 (R 2 ) between available phosphate extracted by Lancaster method and multielement extraction methods were in the order of MehlichⅢ (0.979 *** ) > Kelowna (0.977 *** ) > Modified(Mod.). Morgan (0.553 *** ). For exchangeable cations, there were highly significant correlations between 1M NH4OAc 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.016
GPT teacher head0.210
Teacher spread0.195 · 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.

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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

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