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Record W2968852005 · doi:10.22215/etd/2017-12126

Architecture Against Global Warming: A Research Institute for Sustainable Agriculture

2017· dissertation· en· W2968852005 on OpenAlexaff
Muhammad Dawud

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsCarleton University
Fundersnot available
KeywordsAgricultureAquaponicsSustainable agricultureGlobal warmingGreenhouse gasBusinessFood processingNatural resource economicsEnvironmental planningEnvironmental impact of agricultureGreen RevolutionMonocultureProduction (economics)SustainabilityAgricultural economicsEngineeringEnvironmental scienceClimate changeGeographyPolitical scienceEconomicsEcologyAquaculture

Abstract

fetched live from OpenAlex

Global greenhouse gas emissions are a serious threat to the environment.Since the Industrial Revolution, humans have exponentially released gasses and chemicals into the atmosphere which have resulted in the warming of the earth.One major source stems from agriculture and the indirect processes surrounding it.Current methods of unsustainable agricultural production including intensive monoculture crop cultivation, industrial animal agriculture and extensive food miles.This thesis proposes a Research Institute for Sustainable Agriculture, a facility which allows for the research of alternative methods of sustainable food production, praxis of such ideas and a forum for public awareness and education.The institute will be located on Granville Island, British Columbia.The institute will house innovative agricultural practices, including hydroculture and Clean Meat production.It is hoped that the architecture envisioned will assist in developing more innovative agricultural solutions and raise public awareness and interest surrounding current methods of 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 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)0.003
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 score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0070.007
Open science0.0010.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0200.006

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 head0.035
GPT teacher head0.317
Teacher spread0.282 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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