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.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".