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Record W2769652357 · doi:10.1139/cjss-2017-0094

Kinetics and Thermodynamics of Phosphorus Sorption on Goethites: Effects of Biochar Application

2017· article· en· W2769652357 on OpenAlexaffvenue
Fernanda Alves Cangerana Pereira, Inoka Amarakoon, Francis Zvomuya, Nicholson N. Jeke

Bibliographic record

VenueCanadian Journal of Soil Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of Manitoba
FundersUniversidade Federal de Lavras
KeywordsSorptionBiocharGoethiteChemistryPhosphorusKineticsBioavailabilityEnvironmental chemistrySoil waterInorganic chemistryAdsorptionSoil scienceEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Bioavailability of phosphorus (P) in soils is controlled by, inter alia, the presence of Fe and Al oxides, which readily bind with P. Biochar has been suggested for minimizing P sorption to oxides and, therefore, improving P availability to plants. However, the kinetics and temperature dependence of biochar influence on P sorption are poorly understood. The objective of this study was, therefore, to determine the kinetics and thermodynamics of P sorption by goethite as affected by biochar application at 0 and 40 g kg-1 oxide. Batch equilibration tests were run at 15, 25, and 35ºC, and solution P concentrations were measured 0.5, 1, 3, 6, 9, 12, and 24 h after the start of incubation. Sorption of P by the oxides followed pseudo-first-order kinetics. Biochar application enhanced cumulative P sorption by both oxides, and the increase was greater for Al-goethite. Phosphorus sorption increased as temperature increased from 15°C to 25°C but declined at 35°C. Phosphorus sorption on biochar-amended oxides was associated with low activation energy (Ea) values, indicating that sorbed P in soils containing goethite and Al-goethite could still be plant available. This information will contribute towards a better understanding of processes affecting biochar effects on P fate in soils.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.999

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 communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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 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

Citations5
Published2017
Admission routes2
Has abstractyes

Explore more

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