Integrating continuous stocks and flows into state‐and‐transition simulation models of landscape change
Bibliographic record
Abstract
Abstract State‐and‐transition simulation models ( STSM s) provide a general framework for forecasting landscape dynamics, including projections of both vegetation and land‐use/land‐cover ( LULC ) change. The STSM method divides a landscape into spatially referenced cells and then simulates the state of each cell forward in time, as a discrete‐time stochastic process using a Monte Carlo approach, in response to any number of possible transitions. A current limitation of the STSM method, however, is that all of the state variables must be discrete. Here we present a new approach for extending a STSM , in order to account for continuous state variables, called a STSM with stocks and flows ( STSM ‐ SF ). The STSM – SF method allows for any number of continuous stocks to be defined for every spatial cell in the STSM , along with a suite of continuous flows specifying the rates at which stock levels change over time. The change in the level of each stock is then simulated forward in time, for each spatial cell, as a discrete‐time stochastic process. The method differs from the traditional systems dynamics approach to stock‐flow modelling in that the stocks and flows can be spatially explicit, and the flows can be expressed as a function of the STSM states and transitions. We demonstrate the STSM ‐ SF method by integrating a spatially explicit carbon (C) budget model with a STSM of LULC change for the state of Hawai'i, USA . In this example, continuous stocks are pools of terrestrial C, whereas the flows are the possible fluxes of C between these pools. Importantly, several of these C fluxes are triggered by corresponding LULC transitions in the STSM . Model outputs include changes in the spatial and temporal distribution of C pools and fluxes across the landscape in response to projected future changes in LULC over the next 50 years. The new STSM ‐ SF method allows both discrete and continuous state variables to be integrated into a STSM , including interactions between them. With the addition of stocks and flows, STSM s provide a conceptually simple yet powerful approach for characterizing uncertainties in projections of a wide range of questions regarding landscape change.
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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 | 0.001 | 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".