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
Records
5000
Size
0.5GB
Format
JSON
Provider
MOBIUSI INCMOBIUSI INC
Updated
v1.02026-11-05
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.

Overview

Dataset Insights

Preview

Sample Examples

sample.json
{}

Schema

Technical Specifications

file_namestringFile name
answerstringAn accurate, concise response that states the conclusion supported by the evidence and its applicable scope.
evidencestringThe farming context, observations, experimental information, or other evidence provided to support the answer. If relevant evidence is unavailable, this should be stated explicitly.
questionstringA 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_caveatstringStates 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_reasoningstringExplains how a factor might affect growth performance or feed efficiency through a potential mechanism, grounding the reasoning in the available context and evidence.
management_implicationstringProvides cautious guidance relevant to giant freshwater prawn farming without exceeding what the evidence supports. When evidence is insufficient, it identifies information or validation needed.

Trust

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

FAQ

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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Cite 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?dataset_scene_id=5},
  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}
}

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