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Record W3165557618 · doi:10.1139/cjas-2021-0005

The role of livestock in sustainable food production systems in Canada

2021· article· en· W3165557618 on OpenAlexaffvenueabout
Kim Ominski, Kebebe Gunte, K. M. Wittenberg, Getahun Legesse, Genet Mengistu, Tim A. McAllister

Bibliographic record

VenueCanadian Journal of Animal Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of ManitobaCanadian Science Centre for Human and Animal Health
Fundersnot available
KeywordsSustainabilityLivestockNatural resource economicsBusinessProduction (economics)Greenhouse gasCarbon footprintFood systemsEnvironmental resource managementPopulationFood processingFood securityEnvironmental economicsEnvironmental scienceEconomicsAgricultureGeographyEcologyEnvironmental health

Abstract

fetched live from OpenAlex

Global drivers such as the growing human population, evolving consumer preferences, globalization, and climate change have put pressure on the agri-food sector to produce more livestock products with less land, feed, and water. Taste, nutritional value, cost, convenience, source, animal welfare, and environmental sustainability of food are criteria upon which purchasing decisions are made. In response, an environmental footprint analysis composed of greenhouse gas emissions, nutrient and water use efficiency, water quality, carbon storage, and biodiversity has been completed for many commodities. However, as livestock production systems occur within complex agro-ecosystems, it is extremely challenging to formulate a single overall sustainability metric. There is no “silver bullet” to solve the environmental concerns of all livestock production systems as they operate under different constraints on different landscapes, with different water and nutrient cycles, and soil types. Furthermore, the lack of scientific evidence regarding the interactions between livestock production, human nutritional adequacy, and the health of our environment makes it difficult for consumers to interpret this information and make informed food choices. This review examines these complex interactions and trade-offs, as well as the potential impacts of changes in consumer dietary choice on environmental sustainability, nutritional adequacy, and land use.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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

Citations19
Published2021
Admission routes3
Has abstractyes

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