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Record W2121926445 · doi:10.5539/jas.v4n6p278

Evaluation of Cation Exchange Capacity (CEC) in Tropical Soils Using Four Different Analytical Methods

2012· article· en· W2121926445 on OpenAlexvenueno aff
Fabio Aprile, Reinaldo Lorandi

Bibliographic record

VenueJournal of Agricultural Science · 2012
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsnot available
FundersInstituto Nacional de Pesquisas da AmazôniaConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsCation-exchange capacitySoil waterTropical rainforestEnvironmental scienceOrganic matterRainforestTropicsSoil scienceEnvironmental chemistryChemistryEcologyBiology

Abstract

fetched live from OpenAlex

Four analytical methods for determination of cation exchange capacity (CEC) in tropical soils were tested, aiming to define what the most appropriate based on the requirements: analysis time, degree of reliability and cost of operation. A total of 444 soil samples from the Amazon rainforest and Atlantic rainforest were analyzed in eleven soils types. Organic matter, pH and ions Na+, K+, Ca2+, Mg2+ and H++Al3+ were also analyzed. The influence of the action of fire on the release of ions to the soil was also tested. The results indicated that there was a momentary increase in CEC in the soil after a fire. Tropical soils have a high humidity and acidity, contributing to an overall increase of CEC. Adverse climatic conditions in the tropics affect soil properties, so that practical methods and low cost have the advantage that they can be applied periodically to analyze the quality of the soil.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.218
GPT teacher head0.422
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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

Citations161
Published2012
Admission routes1
Has abstractyes

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