Optimal design of experiments to improve the characterisation of atrazine degradation pathways in soil
Bibliographic record
Abstract
Abstract Contamination of soils with pesticides and their metabolites is a global environmental threat. Deciphering the complex process chains involved in pesticide degradation is a prerequisite for finding effective solution strategies. This study applies prospective optimal design (OD) of experiments to identify laboratory sampling strategies that allow model‐based discrimination of atrazine (AT) degradation pathways. We simulated virtual AT degradation experiments with a first‐order model that reflects a simple reaction chain of complete AT degradation. We added a set of Monod‐based model variants that consider more complex AT degradation pathways. Then, we applied an extended constraint‐based parameter search algorithm that produces Monte‐Carlo ensembles of realistic model outputs, in line with published experimental data. Differences between‐model ensembles were quantified with Bayesian model analysis using an energy distance metric. AT degradation pathways following first‐order reaction chains could be clearly distinguished from those predicted with Monod‐based models. As expected, including measurements of specific bacterial guilds improved model discrimination further. However, experimental designs considering measurements of AT metabolites were most informative, highlighting that environmental fate studies should prioritise measuring metabolites for elucidating active AT degradation pathways in soils. Our results suggest that applying model‐based prospective OD will maximise knowledge gains on soil systems from laboratory and field experiments. Highlights Bayesian model analysis can help to distinguish the active degradation pathway of pesticides. Information on degradation metabolites is crucial to understand pesticide fate. Measurements of specific guilds improve the distinction of active pesticide pathways. Prospective optimal design maximizes information gain in soil sciences.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".