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Record W2809156014 · doi:10.1139/facets-2017-0105

Canada: Playing catch-up on phosphorus policy

2018· article· en· W2809156014 on OpenAlexaffvenueabout
Jessica Ross, Sidney Omelon

Bibliographic record

VenueFACETS · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPhosphorusPhosphate fertilizerAction planNatural resource economicsPotashPlan (archaeology)BusinessNutrient managementFertilizerNutrientPolitical scienceEnvironmental resource managementEnvironmental protectionEnvironmental planningEnvironmental scienceEcologyGeographyEconomicsChemistryManagement

Abstract

fetched live from OpenAlex

The concept of sustainable phosphorus is studied in depth around the world, as the scientific community largely agrees that the non-renewable phosphorus reserves in the form of phosphorite ore must be used judiciously. Unfortunately, many developed countries, including Canada, have yet to implement a phosphorus management plan. The Netherlands, Germany, and Switzerland can be heralded as success stories of effective, committed, cross-sector phosphorus management. We examine factors that contributed to their success and consider how these may be transferred to Canada. We also consider Canadian geographic and research factors and contrast the Canadian policy environment and phosphorus recycling efforts with those in the EU. Finally, we analyze active Canadian and North American phosphorus interest groups and seek to determine why their collective efforts have yet to coalesce around tangible action. Canada produces phosphorus fertilizer from imported deposits of phosphate rock. Canada produces potassium fertilizer from its rich potash mines, making it a global power in nutrient production. It is imperative that Canada earns a respected leadership role in efficient global phosphorus and potassium nutrient management and recycling.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.177
Threshold uncertainty score0.955

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0190.006
Scholarly communication0.0110.004
Open science0.0020.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0170.001

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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations14
Published2018
Admission routes3
Has abstractyes

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