Constraint-based parameter sampling to leverage expert knowledge for conditioning soil biogeochemical models
Bibliographic record
Abstract
Mechanistic models facilitate understanding complex biogeochemical interactions and process chains in soil. However, biogeochemical soil models often have weakly constraint parameters and show sloppiness. That means the dimensionality of parameter spaces is overly large and parameters often cannot be inferred based on available experimental data. Thus, equifinality arises, i.e. many different parameter combinations lead to very similar or identical model predictions. Expert knowledge represents a synthesis of existing knowledge on processes in soil systems that can be used to find viable parameter regions such that models give plausible predictions in line with evidence-based expectations. Here, we present an approach to leverage expert knowledge. This is achieved by formulating expert knowledge in terms of parameter and process constraints that must be fulfilled. Viable parameter sets are then identified by model conditioning using a novel Bayesian constraint-based parameter search algorithm that extends a previously published iterative constraint-based parameter search method. The algorithm successively applies stricter conditions by increasing the minimum acceptable number of process constraints to be satisfied in each iteration. We present the concept of the algorithm and demonstrate a successful application to a complex model simulating biodegradation of the herbicide Atrazine that has a high-dimensional parameter space. The presented approach can be widely applied to other soil biogeochemical models and provides a powerful tool to leverage expert knowledge for constructing robust prior parameter distributions for model sensitivity analysis or calibration.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".