Life Cycle Assessment of Carbon Dioxide Emissions from Shelterbelt Seedling Production and Transportation in Saskatchewan, Canada
Bibliographic record
Abstract
Shelterbelts on Saskatchewan (SK) farms are rows of tree and shrub species established around farmyards and livestock enclosures and within crop fields to serve various roles, including protection against wind and water damage to crops and farm infrastructure, soil erosion and moisture loss. Shelterbelts also can contribute to environmental benefits, most important of which is mitigation of greenhouse gas (GHG) emissions, which has been identified as an important climate change mitigation strategy. In the overall strategy of mitigation of GHGs, there is a need for quantifying emissions of these gases in various mitigation operations, including plating of shelterbelts on farms. In order for a farm to plant a shelterbelt, a seedling has to be produced. This information is not currently available and therefore, was selected as the focus of the study. This research developed a life-cycle assessment of production and transportation of shelterbelt seedlings. It provides details on the processes and emissions of the production and transportation stages in the generation of tree seedlings used to establish a one kilometer long farm shelterbelt. The production and transportation stages for 1,000 shrub shelterbelt seedlings was estimated to generate 2,200 kg of carbon dioxide emissions regardless of species. During these stages of the shelterbelt life-cycle, the primary sources of GHG emissions were energy use for heating and for lighting during seedling growth while transportation of seedlings from the point of production to point of use represented a significantly smaller proportion of overall emissions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".