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Record W6923472924 · doi:10.14288/1.0442319

Urban Food Futures

2024· article· en· W6923472924 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Collections · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsAnthropoceneFutures contractFood systemsShoreFood processingSustainabilityEcosystem servicesRestructuring

Abstract

fetched live from OpenAlex

The Anthropocene is characterized by accelerating, unpredictable, and unprecedented human-induced change which threatens the existence of diverse societies, cultures, species, and ecologies. Underlying these issues are unsustainable socio-ecological systems which separate nature and society enabling the capitalist exploitation and degradation of both. Cities and industrial food production are key sites of these struggles as both symptoms and causes of the Anthropocene. Regenerative food and fiber systems have the potential to reconnect people to place, the web of life, and their bodies in urban novel ecosystems creating better socioecological networks in the process. The south shore line in Vancouver is currently occupied by a novel ecosystem typical of highly altered post industrial sites and transportation corridors with plentiful plant resources and habitat potential. This project proposes a regenerative food & fiber network along an existing post-industrial railway corridor in Vancouver. The comprehensive design creates landscapes and architectures that offer flexible space to encourage and support reciprocal acts of care for the landscape. Site interventions provide opportunities for local community members to plant, tend, harvest, share, and recycle food and materials, activities which contribute to the long-term ecological restoration of the site.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.063
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0070.002
Open science0.0010.006
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0630.007

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 head0.015
GPT teacher head0.219
Teacher spread0.204 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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