Supplementary Data: Sustainability of salmon aquaculture systems and inclusion of local feed ingredients
Bibliographic record
Abstract
The supplementary data provide detailed information on assumptions, experimental design, and supporting calculations underlying the life cycle assessment (LCA) of canola meal (CM) inclusion in Atlantic salmon feeds across cage and recirculating aquaculture systems (RAS). Section A outlines key system boundaries and assumptions, including excluded processes (e.g., infrastructure, packaging), zero volatilization of NH₃ and N₂O in RAS systems, fuel use modelling in SimaPro®, average transport distances for ingredients and feeds, and electricity mix adapted to New Brunswick, Canada. Transportation logistics for canola meal from Saskatchewan to the East Coast were modelled using rail over a 4,000 km distance. Section B compiles all LCA input/output values. Section C details a feeding trial on post-smolt Atlantic salmon, evaluating growth performance and feed conversion across four experimental diets with increasing CM inclusion (0–15%). The trial used 12 tanks (1,200 L each), under controlled conditions (12.9 °C, 25 ppt salinity, 16L:8D photoperiod) over 103 days. No significant effects (P ≥ 0.05) were observed on feed intake or conversion ratio at CM inclusion up to 10%, confirming CM’s suitability at moderate levels. Section D describes the modelling approach for nitrogen (N) and phosphorus (P) fate across the production systems. Section E presents the results of a Monte Carlo uncertainty analysis conducted on four production scenarios (cage and RAS with and without 10% CM inclusion), reporting key metrics such as standard deviation, coefficient of variation, and standard error for each environmental impact category. These supplementary materials ensure transparency and reproducibility of the LCA modelling and support the robustness of the environmental findings.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.509 | 0.107 |
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".