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Record W4285008794 · doi:10.1029/2022gb007407

Changes in Canada's Phosphorus Cycle 1961–2018: Surpluses and Deficits

2022· article· en· W4285008794 on OpenAlexafffundabout
Jiaxin Wang, Zhiming Qi, Elena M. Bennett

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsSte. Anne's HospitalMcGill University
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsCyclingEutrophicationPhosphorusManureFertilizerEnvironmental scienceAgricultureNutrient cycleGeographyNutrientPastureEcologyForestryBiologyArchaeologyChemistry

Abstract

fetched live from OpenAlex

Abstract Human activities have greatly changed global phosphorus (P) cycling, posing urgent challenges related to both supply uncertainty and aquatic eutrophication. However, the long‐term dynamics of P across Canada remain unquantified and under‐explored. Using a material flow analysis model, we quantified temporal dynamics of P cycling in Canadian provinces from 1961 to 2018 and characterized the changes in soil P balances through the study period. We found most Canadian agricultural regions had soil P surpluses except Saskatchewan, where large P deficits (−10‐0 kg ha −1 y −1 ) were detected in almost all study years (except 1961). In 2018, Quebec and Atlantic provinces had the highest P surpluses (34 and 159 kg ha −1 y −1 , respectively), and low P surpluses were observed in Ontario, Manitoba, Alberta and British Columbia (9, 16, 5, and 28 kg ha −1 y −1 , respectively). P surplus was reduced in Quebec and Ontario after the nutrient management regulations were put in place in the 1980s. We demonstrated that P flows in cropland played a larger role in Canada P cycling than pasture. P use efficiency tended to be greatest in the Prairie provinces (0.74 in 2018), and least in the Atlantic provinces (0.12 in 2018). However, the rate of increase was considerably steeper in Ontario and Quebec than other provinces. Reducing inorganic fertilizer and manure application would be the most effective method to reduce remaining P surpluses.

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 score0.165
Threshold uncertainty score0.580

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 science0.0000.001
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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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

Citations15
Published2022
Admission routes3
Has abstractyes

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