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Multi-sectorial assessment of phosphorus in Ontario, Canada: Mapping flows and analysis of the potential for recovery and reuse

2023· article· en· W4385541402 on OpenAlexafffundabout
Edgar Martín-Hernández, Jorge Andres Garcia, Samantha Gangapersad, Tian Zhao, Sidney Omelon, Roy Brouwer, Céline Vaneeckhaute

Bibliographic record

VenueResources Conservation and Recycling · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsMcGill UniversityUniversity of WaterlooUniversité Laval
FundersDirectorate of Environment and Climate Change, IndiaEnvironment and Climate Change CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of Canada
KeywordsPhosphorusReuseEnvironmental scienceMaterial flow analysisEconomic feasibilityProduction (economics)Natural resource economicsEnvironmental engineeringWaste managementEnvironmental economicsEngineeringChemistryEconomics

Abstract

fetched live from OpenAlex

Phosphorus is a key non-renewable element used in multiple economic activities, and notably for food production . It is therefore a critical material whose recovery is gaining interest. This work maps the annual phosphorus flows across Ontario's economic sectors through material flow analysis using open data sources. This information is used to identify potential opportunities for phosphorus recovery and recycling, all while performing an economic assessment to determine the feasibility of phosphorus recovery from different sectors. Up to 86% of phosphorus imports for food production could be covered by recycled phosphorus, with an average recovery cost of 49 CAD/kg of phosphorus. This cost is lower than the estimated economic losses caused by phosphorus releases into the environment, although it is significantly higher than the cost of fossil-based phosphorus products. However, phosphorus recovery costs vary widely for different waste streams, suggesting the need of exploring cooperative approaches for effective phosphorus recovery at regional scale.

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 score0.151
Threshold uncertainty score0.277

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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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

Citations16
Published2023
Admission routes3
Has abstractyes

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