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Record W4224076537 · doi:10.1139/cjss-2021-0189

Antagonistic effect of copper and zinc in fertilization of spring wheat under low soil phosphorus conditions

2022· article· en· W4224076537 on OpenAlexaffvenueabout
Noabur Rahman, Derek Peak, J.J. Schoenau

Bibliographic record

VenueCanadian Journal of Soil Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSoil waterZincChemistryFertilizerBioavailabilityPhosphorusAgronomyMicronutrientStrawHuman fertilizationEnvironmental chemistryNutrientCarbonateEnvironmental scienceInorganic chemistrySoil scienceBiology

Abstract

fetched live from OpenAlex

Sound micronutrient management requires an understanding of nutrient interactions and transformation processes in soil–plant systems which can regulate bioavailability and plant uptake. A series of studies were conducted under controlled environment and field conditions to evaluate wheat response to Cu and Zn fertilization on P-deficient soils from western Canada. The grain and straw yields of wheat were reduced in two (Waskada and Tisdale) of three soils used in the controlled environment study, while yield was not affected at the Echo field site in 2016 when both Cu and Zn sulfate fertilizer were applied at 5 kg·ha –1 rates. Zinc concentration in soil and plant tissues was increased to apparent toxic levels with fertilizer addition in Waskada soils. An imbalance in tissue P:Zn concentration related to micronutrient fertilization was observed in Waskada and Tisdale soils. The availability of Cu and Zn in post-harvest soils was increased with increasing rate of these fertilizers' addition. Chemical and spectroscopic speciation using sequential extraction and X-ray absorption near edge structure, respectively, revealed that Cu and Zn were mostly speciated as carbonate phases, and complexation of these elements with carbonate and phyllosilicate minerals is likely the process controlling bioavailability in the 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)0.001
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 score0.645
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.211
Teacher spread0.202 · 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

Citations6
Published2022
Admission routes3
Has abstractyes

Explore more

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