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Record W1992274033 · doi:10.1071/ea03276

Comparing responses to phosphorus of field pea (Pisum sativum), canola (rape, Brassica napus) and spring wheat (Triticum aestivum)

2006· article· en· W1992274033 on OpenAlexaff
M. D. A. Bolland, R. F. Brennan, PJ White

Bibliographic record

VenueAustralian Journal of Experimental Agriculture · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsDepartment of Environment and Conservation
Fundersnot available
KeywordsCanolaField peaSativumBrassicaAgronomySoil waterShootPisumBiologyPhosphorusHorticultureChemistry

Abstract

fetched live from OpenAlex

The phosphorus (P) requirements of spring wheat (Triticum aestivum L.) are well known for all soils in south-western Australia; but the P requirements of field pea (Pisum sativum L.) and canola (Brassica napus L.), which are grown in rotation with wheat on marginally acidic to alkaline soils in the region, are not known. In a glasshouse study, the P requirements of field pea and wheat were compared for 16 soils collected throughout the agricultural region. Ten of the 16 soils were also used to compare the P requirements of canola and wheat. The P was applied as powdered single superphosphate, and yield of dried shoots of 42-day-old plants was measured. The amount of P required to produce 90% of the maximum yield of dried shoots (PR90 values) was used to compare the P requirements of the species. To produce 90% of the maximum yield, field pea required less P than wheat in 5 soils, similar P in 2 soils, and more P in 9 soils. Canola required less P than wheat in all 10 soils. We conclude the P requirements of field pea or canola relative to wheat depend on a complex interaction between plant and soil, particularly for field pea relative to wheat. Per unit of applied P, the P concentration in dried shoots decreased in the order canola > wheat > field pea, indicating the order in which plant roots of the 3 species were able to access P from soil.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.235
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 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

Citations5
Published2006
Admission routes1
Has abstractyes

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