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Record W3172727538 · doi:10.1093/cdn/nzab060_002

Carbon Footprint of Canadian Self-Selected Diets: Trade-Offs With Nutrient Intakes and Diet Quality

2021· article· en· W3172727538 on OpenAlexaffabout
Olivia Auclair, Sergio A. Burgos

Bibliographic record

VenueCurrent Developments in Nutrition · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsMcGill University
Fundersnot available
KeywordsNutrientFood groupContext (archaeology)Food scienceCarbon footprintAnimal scienceBiologyMedicineGreenhouse gasEnvironmental health

Abstract

fetched live from OpenAlex

Individuals' dietary choices are critical determinants of human and planetary health. Although the environmental impact of animal-based foods typically exceeds that of plants, trade-offs among nutritional outcomes and environmental sustainability in the context of self-selected diets are less understood. The objectives were to estimate the carbon footprint of Canadian self-selected diets and to compare low- and high-GHGE diets in terms of intake of food groups, nutrients, and diet quality. Twenty-four-hour recalls from the 2015 Canadian Community Health Survey (CCHS) – Nutrition were used to determine dietary intake among adults ≥19 y (n = 13,612). Estimates from the database of Food Impacts on the Environment for Linking to Diets were used to link foods and beverages reported in the CCHS to their greenhouse gas emissions (GHGE). Intake of food groups, nutrients, and diet quality based on the Alternative Healthy Eating Index – 2010 were compared between low- and high-GHGE diets (lowest and highest quintiles of dietary GHGE expressed per 1,000 kcal). Dietary GHGE (mean ± SE) was 3.98 ± 0.06 kg CO2-equivalents (eq) per person per d or 2.15 ± 0.03 kg CO2-eq per person per 1,000 kcal. Animal-based foods contributed three-quarters of Canadians' total dietary GHGE, with red and processed meat alone accounting for 47.05 ± 0.82%. High-GHGE diets contained more animal-based foods, but also more vegetables and fruits and miscellaneous foods and beverages; low-GHGE diets contained more cereals, grains, and breads. High-GHGE diet respondents had higher intakes of nutrients of public health concern (iron, potassium, calcium, and vitamin D), but also higher intakes of nutrients to limit (saturated fat and sodium). Moreover, low-GHGE diets had higher diet quality scores compared to high-GHGE diets (55.31 ± 0.49 vs. 47.27 ± 0.46 points; p < 0.0001). Self-selected Canadian diets with the highest GHGE contained more animal-based foods and were characterized by higher intakes of nutrients of concern but a lower overall diet quality. These trade-offs warrant attention in shaping future food policy and dietary guidance in Canada aimed at meeting global targets for climate change. None.

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)0.000
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 score0.041
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.235
Teacher spread0.221 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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