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Record W1984488780 · doi:10.1081/css-120030563

Spatial Variability of Nutrient Requirements in Fields of the South Peace River Region, Alberta

2004· article· en· W1984488780 on OpenAlexaffabout
Cheryl Florence. Fletcher, Muhammad Arshad, R. C. Izaurralde, W. B. McGill

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Geostatistics and Mapping
Canadian institutionsUniversity of Northern British ColumbiaAgriculture and Agri-Food Canada
Fundersnot available
KeywordsFertilizerNutrientTransectEnvironmental sciencePhosphorusSpatial variabilityAgronomyPotashHydrology (agriculture)MathematicsEcologyGeologyChemistryStatisticsBiology

Abstract

fetched live from OpenAlex

Abstract Site-specific fertilizer application is pertinent only if there is a significant sub-field variability in nutrient requirements. The variability in soil test recommendations for nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) was measured, along transects across three fields in the South Peace River region of Alberta. Depending on the field and intended crop, site-specific fertilizer application would: (1) increase fertilizer inputs by 4–10 kg ha−1, (2) reduce fertilizer inputs by 5–30 kg ha−1, or (3) redistribute the same amount of fertilizer required for uniform application, differently among nutrients and across the field. Fields with extreme topography are more likely to benefit from site-specific fertilizer application, however, slope position alone would be inadequate to stratify fields for this purpose. Requirements for K were the most sensitive to sub-field variability, followed by those for S, P, and N. Producers in the region may not consider site-specific application because grid sampling may be too costly. Quick and economical ways to determine site-specific fertilizer requirements are needed.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.260
Teacher spread0.232 · 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
Published2004
Admission routes2
Has abstractyes

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