A simplified logistics model for integrating BIMAT and IBSAL to estimate harvest costs, energy input and emissions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it