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Record W3096199243 · doi:10.1038/s41598-020-75911-y

Response of wheat, pea, and canola to micronutrient fertilization on five contrasting prairie soils

2020· article· en· W3096199243 on OpenAlexaff
Noabur Rahman, J.J. Schoenau

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSoil waterAgronomyCanolaFertilizerMicronutrientStrawChemistrySoil pHSoil fertilityEnvironmental scienceBiologySoil science

Abstract

fetched live from OpenAlex

A polyhouse study was conducted to evaluate the relative effectiveness of different micronutrient fertilizer formulation and application methods on wheat, pea and canola, as indicated by yield response and fate of micronutrients in contrasting mineral soils. The underlying factors controlling micronutrient bioavailability in a soil-plant system were examined using chemical and spectroscopic speciation techniques. Application of Cu significantly improved grain and straw biomass yields of wheat on two of the five soils (Ukalta and Sceptre), of which the Ukalta soil was critically Cu deficient according to soil extraction with DTPA. The deficiency problem was corrected by either soil or foliar application of Cu fertilizers. There were no significant yield responses of pea to Zn fertilization on any of the five soils. For canola, soil placement of boric acid was effective in correcting the deficiency problem in Whitefox soil, while foliar application was not. Soil extractable Cu, Zn, and B concentration in post-harvest soils were increased with soil placement of fertilizers, indicating that following crops in rotation could benefit from this application method. The chemical and XANES spectroscopic speciation indicates that carbonate associated is the dominant form of Cu and Zn in prairie soils, where chemisorption to carbonates is likely the major process that determines the fate of added Cu and Zn fertilizer.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.198
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
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)0.0000.000

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 head0.019
GPT teacher head0.227
Teacher spread0.208 · 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 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

Citations29
Published2020
Admission routes1
Has abstractyes

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