Combining water and pesticide data with coupled surface/subsurface hydrological modeling to reduce its uncertainty.
Bibliographic record
Abstract
In small agricultural catchments over Europe, intensive use of pesticides leads to widespread contamination of rivers and groundwater, largely due to hydraulic transfers of these reactive solutes from plots to rivers. These transfers must be better understood and described in the watershed in order to be able to propose best management practices adapted to the catchment and to reduce its contamination. The physically based model CATHY simulates interactions between surface and subsurface hydrology and reactive solute transport. However, the high sensitivity of pesticide transfers to spatially heterogeneous soil properties induces uncertainty that should be quantified and reduced. In situ data on pesticides in a catchment are usually rare and not continuous in time and space. Likewise, satellite imagery can provide spatial observations of hydrologic variables but not generally of pesticide fluxes and concentrations, and at limited scale and time frequency. The objective of this work is to combine these 3 types of information (model, in situ data, images) and their associated errors with data assimilation methods, in order to reduce pesticide and hydrological variable uncertainties. The sensitivity to spatial density and temporal frequency of the data will be evaluated, as well as the coupled data assimilation efficiency, i.e., the effect of assimilating hydrological data on pesticide-related variables. The methods will be developed using a Python package, and compared/evaluated on twin experiments using virtual data that are however generated over a real vineyard catchment, in Beaujolais, France, in order to ensure realism of the experiments, data, and associated errors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".