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Record W7100356927

Seed Yield Potential of Five Wheat Species/Cultivars without and with Phosphorus Fertilizer Application on a P-Deficient Soil in Northeastern

2015· article· en· W7100356927 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPhosphorusYield (engineering)FertilizerSoil waterProductivityField experiment
DOInot available

Abstract

fetched live from OpenAlex

In the Canadian Prairies, many soils on organic farms are low in available phosphorus (P). Previous research has shown that wheat species/cultivars vary in their sensitivity to P deficiency, yield re-sponse to applied P fertilizer, P uptake and P use efficiency on P-deficient soils. A 3-year field ex-periment was conducted from 2012 to 2014 on a P-deficient soil at Kelvington, Saskatchewan, Canada, to determine the potential of five wheat species/cultivars (Spelt and Kamut representing “ancient”, Red Fife representing “old”, and Unity and Goodeve representing “modern ” wheat spe-cies/cultivars) for seed yield, protein concentration (PC) in seed, partial factor of productivity (PFP, kg seed kg−1 of N applied), total N and P uptake, P use efficiency (PUE, kg seed kg−1 of P ap-plied) and % recovery of applied P in seed under zero-P and with P fertilizer (triple superphos-phate) applied at 20 kg P ha−1. Seed yield, PFP and PUE were determined in all 3 years, but PC, to-tal N and P uptake, and % recovery of applied P in seed were determined only in 2012. There was a marked and significant response of seed yield and PFP of all wheat species/cultivars to P fertil-izer in all 3 years, but the actual seed yield and PFP, without and with applied P fertilizer, as well

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.911

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.028
GPT teacher head0.212
Teacher spread0.184 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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