Nutritional and Environmental Sustainability of Lentil Reformulated Beef Burger
Bibliographic record
Abstract
Numerous studies have shown that replacing a portion of beef with plant-based foods in daily diets of high-income nations can improve health, nutrition, and environmental consequences globally. Pulses are one of the major plant-based protein foods shown to have both environmental and nutritional benefits. For consumers to adopt more plant-based foods in their diets, more options are needed that meet consumer demands for taste, convenience, nutrition, and sustainability along with dietary preferences. Beef-based burger patties can be made more sustainably, nutritiously, and cost-effectively while maintaining palatability by reformulating with a portion of pulses such as whole cooked lentils. The aim of this study was to quantify the nutritional and environmental benefits of such lentil-reformulated beef burgers. Here we compared the nutrient balance score (considering 27 essential macro and micronutrients) and environmental footprints (carbon, bluewater, water scarcity, land use, and biodiversity) of an all-beef burger with a beef burger reformulated with a portion of cooked lentil puree. The geographic resolution of the analysis was Saskatchewan, Canada. Results showed that partial replacement of a lean beef burger with cooked lentil puree increased the nutrient density by ~20%, decreased the life cycle environmental footprint by ~33%, and reduced the cost by 26%. In particular, the lentil reformulated burger had 60 times higher dietary fiber, three times higher total folate, five times higher manganese, and 1.6 times higher selenium than the all-beef burger. We highlight the importance of using high-spatial resolution inventory of agricultural inputs and characterization factors (impacts per unit agricultural inputs) to obtain more accurate environmental results. The results underscore the potential of food innovation to contribute towards multiple global sustainable development goals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".