Assessing the Circularity of Nutrient Flows Across Nested Scales for Four Food System Scenarios in the Okanagan Bioregion, BC Canada
Bibliographic record
Abstract
In light of continued nutrient pollution in water bodies and anticipated insecurities related to future nutrient supplies, there is an increasing awareness of the need to use nutrients in a more circular way. As part of a food system design study in the Okanagan bioregion, BC Canada we set out to evaluate different food system scenarios for the year 2050 in terms of nutrient circularity. In doing so, the objective was to evaluate the circularity of nutrient flows not only in the Okanagan, but also in relation to exogenous regions, insofar as nutrient flows relate to feed and food consumption and production in the Okanagan. This is important because feed and food trade means that nutrient inputs to crop production in the Okanagan may make their way into organic residuals outside the Okanagan, and vice versa. If not accounted for, this may lead to a distorted picture when analyzing nutrient circularity. To this effect, we applied an analytical framework and calculation model that explicitly tracks nutrients from crop production to organic residual generation. The results of the study suggest that assessing nutrient circularity across nested scales was critical for two reasons. First, changes in overall nutrient flows in response to population increase and dietary change were found to be more pronounced outside the Okanagan. Second, our analysis clearly revealed the extent to which feed and food trade boost nutrient self-reliance in the Okanagan at the expense of nutrient self-reliance outside the Okanagan. This kind of analysis should therefore be useful to explore, ideally together with food system and organic residual management actors, how different food system and organic residual management scenarios perform in terms of nutrient circularity, in the geographical area being considered, but also how it impacts nutrient flows and circularity in the places with which feed and food are traded.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".