An Integrated Framework for Regional Assessment of Water, Energy, and Nutrients from Food Loss of Selected Crops in the Lower Fraser Valley, Canada
Bibliographic record
Abstract
Although there is no global shortage of food or water, food security has not been achieved, as human activity has turned these vital resources into “waste”. Wasted food not only loses valuable water resources but embedded calories of human energy and nutrients for healthy human populations. The Food and Agricultural Organization of the United Nations, in addressing these concerns, focuses on a global scale largely on an economic estimate of individual components of energy or water or nutrient loss. It is suggested that more information is required through local or regional assessments to provide better estimates, incorporating regional factors of the losses along the food supply chain. To address this suggestion, this study focused on an intensive agricultural and rapidly urbanizing region of Canada, the Lower Fraser Valley of British Columbia. Seven selected crops, including annual crops such as green peas, sweet corn and potato, and perennial crops that included three berry crops were assessed for their water, both constituent and virtual, as well as embedded energy, protein, and Vitamin C. Annual virtual water losses were higher for sprinkler than drip irrigation, ranging from 82 × 106 kg of water for strawberry to 7570 × 106 kg for blueberry. These high virtual water losses estimated along the food chain confirm the significance of food loss impacts on local water resources. Estimates of losses of food in kg were highest at the consumer level along the food chain and it was estimated that wasted food from the seven crops selected would have supplied the protein and caloric energy of over 33,000 men per year and Vitamin C of about 240,000 men per year. This assessment increases the awareness of food loss impacts from a regional perspective and provides a framework for future research on both environmental and nutritional implications of wasted food.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".