Watershed Nutrient Pollution Source Counterfactual QA Dataset

#counterfactual reasoning #nutrient pollution source analysis #evidence-based reasoning #watershed nutrient pollution management #water quality assessment #pollution source impact analysis

5000
0.3GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-04

AI Analysis & Value Prop

This dataset provides counterfactual question-answer examples about nutrient sources in watersheds, including agricultural runoff and wastewater discharge. Examples describe baseline watershed and water-quality conditions, interventions that remove or change a source, relevant evidence, and comparative conclusions. Answers state assumptions, cautiously assess likely changes in nutrient levels and water quality, and identify evidence limits, supporting training and evaluation for environmental reasoning and water-quality impact analysis.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
question	string	Asks how the specified source intervention would likely affect nutrient conditions or water quality compared with the baseline.
assumptions	string	States the assumptions required for the counterfactual comparison, such as other nutrient sources and environmental conditions remaining unchanged.
evidence_notes	string	Summarizes observations, studies, or other evidence relevant to the baseline and counterfactual comparison, including known limitations.
evidence_boundary	string	Explains what the available evidence supports and identifies uncertainty, missing information, or limits on causal attribution.
watershed_scenario	string	Describes the watershed, baseline conditions, relevant nutrient levels, and context needed to interpret the scenario.
source_intervention	string	Specifies the nutrient source removed or changed in the counterfactual scenario and any assumptions about the intervention.
counterfactual_answer	string	Compares the likely water-quality outcome under the counterfactual with the baseline using appropriately qualified language.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the dataset contain?: Each example is organized around a watershed scenario and includes baseline conditions, a nutrient-source intervention, relevant evidence, a question, and a counterfactual answer. Assumptions and evidence boundaries can also be specified for the comparison.

How does it handle uncertainty?: Answers compare the intervention scenario with the baseline using qualified, conditional language. They state assumptions, such as other sources or environmental conditions remaining unchanged, and identify gaps in evidence or limits on causal attribution.

Which training and evaluation tasks does it support?: It supports counterfactual and causal reasoning, nutrient pollution source analysis, and water-quality impact assessment. It can also help evaluate whether a model gives appropriately cautious environmental science answers based on the evidence provided.

How can I obtain the data and confirm its license?: Use the dataset product page or the provider's designated channel to review and obtain the data. Availability, download options, and permitted uses depend on the applicable product listing and license; confirm that the terms cover your intended research or commercial use.

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Cite this Work

@dataset{Mobiusi2026,
  title={Watershed Nutrient Pollution Source Counterfactual QA Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/7ee1365ea807fcc97ce44e171acbf09d?dataset_task_cate_id=6},
  urldate={2026-10-19},
  keywords={watershed nutrient pollution,counterfactual QA dataset,water quality change analysis,agricultural runoff impact,wastewater discharge assessment,environmental causal reasoning,nutrient source analysis,environmental QA training},
  version={1.0}
}

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