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Soil amendments reduce P release from flooded soils: Incubation studies simulating snowmelt and summer flooding

2020· article· en· W3094522526 on OpenAlexaffabout
Chammi P. Attanayake, Darshani Kumaragamage, Chamara S. Weerasekara, U.W.A. Vitharana, Saman Dharmakeerthi, Emily Van, Doug Goltz, Srimathie P. Indraratne

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of ManitobaUniversity of Winnipeg
Fundersnot available
KeywordsSoil waterSnowmeltChemistryFerricManganeseAnimal scienceEnvironmental chemistryLeaching (pedology)Surface runoffEnvironmental scienceEcologyInorganic chemistryBiologySoil science

Abstract

fetched live from OpenAlex

Flooding caused by snowmelt runoff in the spring and early summer and heavy rainfall in the summer could enhance P release into nearby surface water bodies causing eutrophication. Six soil amendments were tested for their effectiveness in reducing P release from flooded-soils. Soils were collected from the flood-prone fields in the Red River Valley region in Manitoba, Canada. The tested amendments were gypsum, magnesium sulphate, alum, ferric chloride, zeolite and manganese oxides. Intact soil columns were subjected to flooding for 8 weeks at 4oC simulating the snowmelt in the spring and the early summer and at 22oC simulating flooding occurrences in the summer. Release of soil P into soil solution and floodwater was higher at 22oC than that at 4oC. Gypsum, magnesium sulphate, alum and ferric chloride were effective in reducing the concentrations of P in the pore- and flood-water at various capacities. Ongoing research on zeolite and manganese oxide suggests that manganese oxide was more effective in reducing soluble P concentrations in soils at early days of flooding.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.055
GPT teacher head0.278
Teacher spread0.223 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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