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Record W3127748301 · doi:10.5539/jas.v13n3p46

Growth and Yield of Corn, Carrot and Onion Treated With Rock Phosphate Organic Fertilizer Grown in Standoff Soil Southern Alberta, Canada

2021· article· en· W3127748301 on OpenAlexaffvenueabout
Adebusoye O. Onanuga, Roy Weasel Fat, Roy M. Weasel Fat

Bibliographic record

VenueJournal of Agricultural Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsRed Crow Community College
Fundersnot available
KeywordsPhosphoriteFertilizerCropAgronomyPhosphateYield (engineering)Environmental scienceHorticultureChemistryBiology

Abstract

fetched live from OpenAlex

An experiment was performed in Standoff, Southern Alberta to investigate resource cheap rock phosphate organic fertilizer application to corn, carrot and onion plots. The objective of the study was to ascertain effectiveness of rock phosphate organic fertilizer to support growth and yield of corn, carrot and onion crops grown in Southern Alberta. The varying levels of rock phosphate at 50 P kg/ha for Low P, 100 P kg/ha for High P and control were applied to corn, carrot and onion plots. These treatments were replicated three times, resulting into nine plants per crop. Agronomical parameters collected were subjected to analysis of variance using Duncan Multiple Range Test for separation of means. Result of the experiment indicated that Low P and High P favoured corn height and number of leaves but did not support other parameters measured due to inadequate rock phosphate applied. It was observed that rock phosphate influenced residual level of P after harvest of corn, carrot and onion. Onion plots had the highest P left in the soil than corn and carrot plots. This studies showed potential of rock phosphate in crop production, if apply in adequate amount and availability of soil moisture, as well as high residual P in the soil after harvest.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.158
Teacher spread0.154 · 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

Citations3
Published2021
Admission routes3
Has abstractyes

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