Giant Freshwater Prawn Growth and Feed Efficiency Causal Reasoning QA Dataset
#causal reasoning #question answering #evidence-based analysis #freshwater prawn aquaculture #feed efficiency optimization #aquaculture growth management
- 5000
- 0.5GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-10-25
AI Analysis & Value Prop
This dataset provides question-and-answer text about growth performance and feed efficiency in giant freshwater prawns, covering feed, feeding methods, stocking density, water temperature, molting stage, and other farming factors. Each record includes a question, background and evidence, an answer, causal reasoning, a causal caveat, and a management implication, supporting evidence-based analysis of potential mechanisms and the distinction between correlation and causation. It can support aquaculture management QA, causal reasoning evaluation, and reasoning-focused supervised fine-tuning (SFT); no fixed sample count or coverage statistics are provided.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| answer | string | An accurate, concise response that states the conclusion supported by the evidence and its applicable scope. |
| evidence | string | The farming context, observations, experimental information, or other evidence provided to support the answer. If relevant evidence is unavailable, this should be stated explicitly. |
| question | string | A question about how feed, feeding methods, stocking density, water temperature, molting stage, or other farming factors may affect giant freshwater prawn growth or feed efficiency. |
| causal_caveat | string | States whether the available information supports a causal conclusion, clarifies that correlated changes do not establish causation, and identifies possible confounders, alternative explanations, or evidence gaps. |
| causal_reasoning | string | Explains how a factor might affect growth performance or feed efficiency through a potential mechanism, grounding the reasoning in the available context and evidence. |
| management_implication | string | Provides cautious guidance relevant to giant freshwater prawn farming without exceeding what the evidence supports. When evidence is insufficient, it identifies information or validation needed. |
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 topics does the dataset cover?
- It focuses on growth performance and feed efficiency in giant freshwater prawns. Questions address feed, feeding methods, stocking density, water temperature, molting stage, and other farming factors, examining how they relate to aquaculture outcomes.
- How does the dataset support causal reasoning training?
- Each record includes a question, background and evidence, an answer, causal reasoning, a causal caveat, and a management implication. Models can learn to explain possible mechanisms, state the limits of a conclusion, and identify confounders or alternative explanations when evidence is insufficient.
- What are the dataset size and evidence quality?
- The available information does not specify the number of samples, data sources, or experimental design, so size and representativeness cannot be assessed from it alone. Before use, check the evidence source, observation conditions, and scope of each conclusion. Records should state when relevant evidence is missing.
- How can I access the data, and what are its licensing and use terms?
- Obtain the dataset through its listing or hosting platform, and follow the format, version, and license terms stated there. It is suited to giant freshwater prawn management QA, causal reasoning research, and supervised model fine-tuning; production decisions should also account for local farming conditions and expert validation.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Giant Freshwater Prawn Growth and Feed Efficiency Causal Reasoning QA Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/1265cf5b77600b6f2722e62af4f8b867},
urldate={2026-10-08},
keywords={giant freshwater prawn aquaculture, prawn growth data, feed efficiency, aquaculture causal reasoning, aquaculture management QA, aquaculture SFT, feeding methods, stocking density},
version={1.0}
}Using this in research? Please cite us.
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