Does pattern matter?: spatio-temporal modelling strategies to predict grassland productivity dynamics, Grasslands National Park, Saskatchewan
Bibliographic record
Abstract
Environmental models allow scenario testing and experiments that are impossible to address in the field or laboratory, and are therefore important tools in environmental management. The simplification of real-world processes creates inherent uncertainty in the predictions, however, and it is important to include assessment of sources, propagation, and management of this uncertainty in modelling. This research demonstrates advantages of various models, and recommendations to reduce uncertainty, focussing on models of net primary productivity (NPP) in Grasslands National Park, Saskatchewan. First, I examine the sensitivity of productivity to climate change, using the CENTURY model. Stability of NPP, which is more ecologically relevant than individual annual predictions, is predicted to decrease if there are more than small increases to the variability of precipitation in future climates. However, changes to precipitation variability also markedly increased the uncertainty of the NPP predictions. This uncertainty could be reduced with spatially distributed predictions, and a finer temporal resolution. Second, I adapt a spatially-explicit modelling framework (RHESSys) from forestry to work in grasslands. Changes were made to treatment of soil moisture, phenology, and photosynthesis to successfully predict grass NPP. Uncertainty introduced by the spatial framework could be reduced through better understanding of the role of spatial partitioning. Third, I address the partitioning issue by using alternate landscape representations with RHESSys and comparing their ability to create homogeneous modelling units. Grids, shifted grids, quadtrees, and hillslope partitions are compared to select an intelligent choice. Recommendations are made on how such modelling could be used in a less research-intensive environment. Fourth, I examine light use efficiency (LUE) models, which can be adapted very quickly for management purposes. The effects of drought on LUE need to be incorporated to obtain realistic predictions. This allows spatially extensive estimates of NPP driven by satellite images, and can assist with management decisions for the region, and monitor their effects. Together, this work covers a wide range of modelling and geographic information management with very different data and setup needs. The combination of science and technology allows a range of different questions, and I present recommendations for their appropriate use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".