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Record W7095330585

RESEARCH ARTICLE Phosphorus Cycling in Montreal’s Food and Urban Agriculture Systems

2016· article· en· W7095330585 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Comparative Analysis Research
Canadian institutionsnot available
Fundersnot available
KeywordsCyclingUrban agricultureMaterial flow analysisAgriculturePhosphorusFood wasteFood systemsUrban waste
DOInot available

Abstract

fetched live from OpenAlex

Cities are a key system in anthropogenic phosphorus (P) cycling because they concentrate both P demand and waste production. Urban agriculture (UA) has been proposed as a means to improve P management by recycling cities ’ P-rich waste back into local food pro-duction. However, we have a limited understanding of the role UA currently plays in the P cycle of cities or its potential to recycle local P waste. Using existing data combined with sur-veys of local UA practitioners, we quantified the role of UA in the P cycle of Montreal, Cana-da to explore the potential for UA to recycle local P waste. We also used existing data to complete a substance flow analysis of P flows in the overall food system of Montreal. In 2012, Montreal imported 3.5 Gg of P in food, of which 2.63 Gg ultimately accumulated in landfills, 0.36 Gg were discharged to local waters, and only 0.09 Gg were recycled through composting. We found that UA is only a small sub-system in the overall P cycle of the city, contributing just 0.44 % of the P consumed as food in the city. However, within the UA sys-tem, the rate of recycling is high: 73 % of inputs applied to soil were from recycled sources. While a Quebec mandate to recycle 100 % of all organic waste by 2020 might increase the role of UA in P recycling, the area of land in UA is too small to accommodate all P waste pro-duced on the island. UA may, however, be a valuable pathway to improve urban P sustain-ability by acting as an activity that changes residents ’ relationship to, and understanding of, the food system and increases their acceptance of composting.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.136
GPT teacher head0.457
Teacher spread0.321 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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