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Record W4388409541 · doi:10.1038/s41893-023-01241-2

Food without agriculture

2023· article· en· W4388409541 on OpenAlexaff
Steven J. Davis, Kathleen C. Alexander, Juan Moreno‐Cruz, Chaopeng Hong, Matthew R. Shaner, Ken Caldeira, Ian McKay

Bibliographic record

VenueNature Sustainability · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of ChinaClimateWorks FoundationU.S. Department of AgricultureNational Science Foundation
KeywordsAgricultureGreenhouse gasSustainable agricultureFood systemsNatural resource economicsEnvironmental scienceBusinessEnvironmental impact of agricultureSustainabilityPalm oilEcological footprintCarbon footprintAgricultural economicsEnvironmental protectionAgricultural scienceFood securityGeographyEconomicsEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Efforts to make food systems more sustainable have emphasized reducing adverse environmental impacts of agriculture. In contrast, chemical and biological processes that could produce food without agriculture have received comparatively little attention or resources. Although there is a possibility that someday a wide array of attractive foods could be produced chemosynthetically, here we show that dietary fats could be synthesized with <0.8 g CO 2 -eq kcal −1 , which is much less than the >1.5 g CO 2 -eq kcal −1 now emitted to produce palm oil in Brazil or Indonesia. Although scaling up such synthesis could disrupt agricultural economies and depend on consumer acceptance, the enormous potential reductions in greenhouse gas emissions as well as in land and water use represent a realistic possibility for mitigating the environmental footprint of agriculture over the coming decade.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.004
GPT teacher head0.239
Teacher spread0.235 · 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

Citations30
Published2023
Admission routes1
Has abstractyes

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