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Record W3193581814 · doi:10.5304/jafscd.2021.104.001

Controlled environment agriculture and containerized food production in northern North America

2021· article· en· W3193581814 on OpenAlexafffund
Alex Wilkinson, Craig Gerlach, Meriam G. Karlsson, Henry Penn

Bibliographic record

VenueJournal of Agriculture Food Systems and Community Development · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Calgary
FundersPeter Gilgan FoundationUniversity of CalgaryArctic Institute of North America
KeywordsFood systemsAgricultureContext (archaeology)Food processingProduction (economics)BusinessFood securityNatural resource economicsMarketingGeographyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

There is an ongoing debate about the role of con­trolled environment agriculture and containerized food production in local food systems in Northern North American communities. Some critics dismiss these applications as ineffective, arguing that because they marginalize certain populations they do not have a place in northern food systems. However, such critiques are premature and under­mine what may prove to be an important and com­plementary component of local and regional food systems in the north, particularly if designed and implemented in a culturally appropriate and place-based context. Containerized food production can offer enhanced food production capabilities for communities through year-round production. While there are still concerns about proper growing protocols, scalability, output, durability, and economics, these can be addressed, modified and improved through research and continued applica­tions. New opportunities requiring further explora­tion in the application of containerized food pro­duction systems include, but are not limited to, integrative systems design, the enhancement of community development initiatives, and the inte­gration of the social networks that are necessary for diversified local food production.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.252
Teacher spread0.231 · 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.

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

Citations5
Published2021
Admission routes2
Has abstractyes

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