WS3: An open-source Python framework for integrated simulation and optimization of forest landscape and wood supply systems
Bibliographic record
Abstract
Transparent decision support for forest landscapes demands integrated scheduling, carbon account-ing, and spatial reporting. WS3 is an open-source Python framework that unifies modular simu-lation, solver-backed Model I optimization, and raster allocation in a single workflow. The systemingests Woodstock-style inventories, actions, and scenarios; exposes an explicit data model; andautomates spatial/aspatial conversions. Native linkage to the Canadian Forest Service CarbonBudget Model (libCBM) provides carbon stock and flux estimation for Canada and other libCBM-calibrated jurisdictions. We document the architecture, mathematical formulation, and reproduciblecase studies that pair optimization-driven harvest scheduling with libCBM and spatial allocationto illustrate policy trade-offs in harvest flows, carbon dynamics, and disturbance footprints. WS3ships with an open reproduction package, documentation, and Zenodo-archived releases that ensuredeterministic builds of figures, tables, and parity tests. The framework lowers barriers to auditable,climate-aware forest planning for researchers, agencies, and practitioners.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".