Carbon and Greenhouse Gas Accounting of Forest Operations in FPInterface
Bibliographic record
Abstract
Developed by FPInnovations, FPInterface is an operational-level simulation platform for forest supply activities from the harvest site to the mill gate. The software can model, simulate and optimize forest operations directly from the GIS planning maps. The analysis is done at the block level for a forest management unit and provides the cost and volumes of all products harvested from the selected blocks. The basic platform allows for cost calculations of harvesting, road construction, transport and regeneration. Additional modules are also available for optimizing transport routes, biomass supply flow and cost estimates, operational scheduling and value chain decisions. The software offers a tactical and operational forest planning tool in the context of Canadian forest operations.FPInterface considers all fossil fuel inputs and biomass outputs based on product specifications, harvesting decisions, equipment selection, road network and stand conditions. Therefore, the software offers an opportunity for the development of functionalities for greenhouse gas emissions accounts and carbon budgets for woody feedstock. The main objective of this paper is to describe the calculation of carbon ratio in a module of the FPInterface software. Furthermore, a scenario analysis was conducted, where the usability of the module was demonstrated. The objectives of the analysis were to show the impact of tree size on carbon emissions and to compare different supply chains for biomass in terms of carbon ratios.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".