A simplified logistics model for integrating BIMAT and IBSAL to estimate harvest costs, energy input and emissions
Bibliographic record
Abstract
The Agriculture and Agri-Food’s Biomass Inventory Mapping and Analysis Tool (BIMAT) provides internet-based GIS functionality to query and visualize biomass inventory data in Canada. The Integrated Biomass Supply Analysis and Logistics (IBSAL) model is a modularized simulation of biomass supply chain. In this study, IBSAL modules are assembled to simulate harvesting of straw, stover, and switchgrass yields. The operations in this study started from combining for grain crop residues and ended in stacking bales on the field side. The equation C=aR^b Y^c was fitted to the simulated data to estimate constants a, b, and c for cost in $/dry tonne, energy input in MJ/dry tonne, and carbon emissions in kg CO2/dry tonne. Variable R is the fraction of above ground biomass removed during harvest and Y is the yield defined as biomass above ground (dry tonne/ha). These functions are supplied to the BIMAT portal and developed specific values for costs, energy input, and emissions on the map. The farm gate cost cost for the stacked bales ranged from $20 per dry tonne for high yielding regions of southwest Edmonton and Ontario to $27 per dry tonne for the eastern Ottawa region, and $31 per dry tonne for low yielding regions of central Saskatchewan. The costs are validated with published custom rates. It is recommended that the next step is to integrate IBSAL and BIMAT codes so the logistics values are generated and shown automatically on the map.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".