100% Great Lake Fish Ontario Supply Chain Analysis. Commissioned by the Conference of Great Lakes and St. Lawrence Governors and Premiers
Bibliographic record
Abstract
Southern Ontario offers valuable insights into the supply chain dynamics of fish harvesting and processing across the Great Lakes region. This report analyzes the existing supply of raw materials produced by fish processing plants and evaluates the feasibility of integrating these resources into other markets. The synthesized report compiles and integrates information from various databases. The analysis examines the quantities and rhythms of fish components from Walleye and Yellow Perch. The findings reveal that combining components from the two species can mitigate supply volatility throughout the year. Specifically, the analysis shows that the standard deviation of the weekly proportion of multi-species heads (1.09%) is smaller compared to the standard deviation of individual species - Walleye (1.27%) and Yellow Perch (1.45%). The reduced standard deviation for multi-species data suggests that incorporating diverse species smooths out the extreme fluctuations in weekly supply, making it more attractive for new markets to incorporate these materials into their value chains. Additionally, the report identifies several viable upcycling models for repurposing fish by-products and highlights transformative upcycling models from other regions. The implications of this analysis suggest that substantial quantities of fish discards could lead to a broad range of upcycling opportunities for processing plants. By exploring these options, plants can enhance their economic and ecological performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".