Strategies for phosphorus recovery in livestock operations: Assessing decentralized and distributed recovery systems
Bibliographic record
Abstract
Phosphorus recovery and recycling are crucial to ensure the sustainable supply of this resource while preserving soils and water bodies. Livestock operations are a major source of phosphorus releases through manure generation. Manure storage and over-application may result in the transportation of phosphorus to water bodies by runoff, leading to nutrient pollution. However, their lack of economies of scale and spatial scattering hinders phosphorus recovery at livestock operations. To address these issues, this work explores the implementation of decentralized and distributed strategies for phosphorus recovery at livestock operations through manure processing centers and mobile processing units, respectively. A multi-objective optimization model is developed to design optimal networks for each strategy studied considering environmental and economic aspects. The optimal location of processing centers is determined according to the processing capacity of each phosphorus recovery technology, the location of the livestock operations, and the manure generated by each operation. The decentralized and distributed strategies for phosphorus recovery are assessed using the State of Minnesota in the USA as a case study. Phosphorus recovery can be performed using a decentralized recovery system with minimum greenhouse gas emissions, resulting in an investment cost of 45 million USD. Nevertheless, integrating a distributed phosphorus recovery approach based on mobile units in the decentralized system results in a more cost-effective recovery strategy. This strategy is also estimated to reduce the cost of phosphorus recovery at livestock operations to 23 million USD.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".