Roundwood Dataset Labeling
Budget: $30 – $250 USD
We are seeking a skilled individual to assist in labeling our roundwood dataset, consisting of approximately 1200 samples. The primary objective is to classify each sample into quality classes B, C, or D, with B representing the highest quality and D the lowest. Each sample requires thorough examination and precise labeling, accompanied by a detailed explanation for its classification and an estimation of uncertainty.
Responsibilities:
- Data Labeling: Review each sample in the dataset and assign it to the appropriate quality class (B, C, or D) based on predefined criteria.
- Explanation: Provide a comprehensive description for each sample's classification, detailing the specific features and characteristics that led to its placement in a particular quality class.
- Uncertainty Estimation: Assess and document the level of uncertainty associated with each classification, considering factors such as data variability and subjective interpretation.
- Preparation and Training:
Before commencing the labeling task, you will undergo an introductory training session to familiarize with the dataset and the criteria for quality classification. This training will include:
- Detailed explanation of quality criteria for each class (B, C, D).
- Identification of significant features for quality assessment.
- Instruction on data labeling guidelines and procedures.
Skills and Qualifications:
- Strong attention to detail and accuracy in labeling.
- Clear communication skills to articulate classification rationale and uncertainty estimation.
- Background in forestry, wood science, or related fields is preferred but not mandatory.
Duration:
The project is expected to be completed within 25 hours.
Responsibilities:
- Data Labeling: Review each sample in the dataset and assign it to the appropriate quality class (B, C, or D) based on predefined criteria.
- Explanation: Provide a comprehensive description for each sample's classification, detailing the specific features and characteristics that led to its placement in a particular quality class.
- Uncertainty Estimation: Assess and document the level of uncertainty associated with each classification, considering factors such as data variability and subjective interpretation.
- Preparation and Training:
Before commencing the labeling task, you will undergo an introductory training session to familiarize with the dataset and the criteria for quality classification. This training will include:
- Detailed explanation of quality criteria for each class (B, C, D).
- Identification of significant features for quality assessment.
- Instruction on data labeling guidelines and procedures.
Skills and Qualifications:
- Strong attention to detail and accuracy in labeling.
- Clear communication skills to articulate classification rationale and uncertainty estimation.
- Background in forestry, wood science, or related fields is preferred but not mandatory.
Duration:
The project is expected to be completed within 25 hours.