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Record W2982388203 · doi:10.1139/cjss-2019-0023

Phosphorus accumulation in Canadian agricultural soils over 30 yr

2019· article· en· W2982388203 on OpenAlexafffundvenueabout
Keith Reid, Kimberley D. Schneider

Bibliographic record

VenueCanadian Journal of Soil Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsManureEutrophicationFertilizerEnvironmental scienceSoil waterAgricultureHydrology (agriculture)PhosphorusManure managementWatershedAnimal scienceNutrientGeographyAgronomySoil scienceChemistryEcologyBiology

Abstract

fetched live from OpenAlex

Phosphorus (P) loss to freshwater is a key driver of eutrophication, and understanding the scale and spatial distribution of potential P sources is a key pre-requisite for implementing policies for P management to minimize environmental impacts. Soil test P (STP) is a useful indicator of the accumulation of P in soils, but these data are not readily available for most agricultural land in Canada, so the cumulative P balance (P inputs as manure or fertilizer minus removal of P in crops) is calculated as a proxy for this value. Cumulative P balance is an important calculation within the indicator of risk of water contamination by P, so allocations of manure and fertilizer P to cropland were updated within the calculation of P balance, and for Ontario, data from 1961 to 1980 were added to account for P applications during that period. The STP concentrations were calculated from the resulting cumulative P balances. When compared with reported STP concentrations, the predicted concentrations showed a statistically significant regression at the national (R 2 = 78%) and provincial scale (Ontario, R 2 = 36%; Prince Edward Island, R 2 = 36%; Manitoba, R 2 = 72%; British Columbia, R 2 = 40%). There was significant variation in the cumulative P balance across Canada, with the highest values corresponding with areas of high livestock density, whereas large zones of P deficit were detected across the Prairies.

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 score0.022
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.213
Teacher spread0.205 · 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

Citations38
Published2019
Admission routes4
Has abstractyes

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