A biomass supply network for Manitoba optimized to improve water quality ecosystem services
Bibliographic record
Abstract
This thesis describes research to model how a biorefinery supply network can be optimized for Manitoba. Using spatial optimization methods, the analysis considers an efficient biomass supply network for a hypothetical biorefinery, then examines how the configuration could change if it were optimized for enhancing ecosystem services by reducing phosphorus loading to Lake Winnipeg. A computer model, SpatialES, was designed for this analysis, and intermediate outputs of the model revealed the phosphorus fertilizer balance for Manitoba, the cost of phosphorus pollution, and the supply of biomass feedstocks. The analysis considered agricultural waste biomass as well as biomass from riparian areas and wetlands. This thesis has made several contributions in biosystems engineering. A main finding is that Manitoba farmland is accumulating phosphorus from fertilizer excess at a rate of 3.6 kg/ha/yr. This surplus has the potential to eventually run off, further loading Lake Winnipeg. Water quality measurements indicate lower loading of 0.98 kg/ha from agriculture, suggesting that the surplus is not yet reaching the lake. The contamination of Lake Winnipeg by phosphorus loading has significant costs in terms of loss of ecosystem services. The total cost of loss of Lake Winnipeg ecosystem services due to phosphorus over-enrichment is approximately $371M/yr. The ultimate objective of this thesis has been to model an optimal biorefinery supply network for a single hypothetical biorefinery in Manitoba demanding cellulosic biomass as a feedstock. As the emphasis of this analysis is the dynamics of biomass supply, the biorefinery product is not defined, as there are multiple products in development and presently few facilities operating commercially at scale. This work affirms that an optimal biorefining network is achievable and will result in reduced nutrient loading. The model tested the implications of an ecosystem services payment to incent wetland and riparian biomass based on potential for reduced phosphorus loading. To increase use of all biomass, including non-conventional wetland and riparian biomass, measures must be taken to increase demand for biomass. Future work on this topic should involve additional spatial optimization to test different bioproduct supply and demand scenarios, and to test different assumptions.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".