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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
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Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
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Calibrating a Transformer-Based Model's Confidence on Community-Engaged Research Studies: Decision Support Evaluation

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Deep learning models can overestimate performance in community-engaged research (CEnR) classification. Calibrated confidence scores may be misleading, highlighting the need for robust AI evaluation to prevent bias.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Community-Engaged Research (CEnR)

Background:

  • Deep learning (AI) excels in classification tasks but risks overfitting and bias in real-world applications.
  • Community-Engaged Research (CEnR) involves collaboration between academics and community partners.
  • Automating decisions with AI necessitates rigorous evaluation to ensure reliability and prevent bias.

Purpose of the Study:

  • Evaluate transformer-based AI models for classifying CEnR studies.
  • Assess the utility of calibrated confidence scores in AI decision support.

Main Methods:

  • Compared 3 domain experts' classifications of 45 CEnR studies against 3 transformer-based AI models.
  • Investigated the role of calibrated confidence scores in AI support for complex decisions.

Main Results:

  • Some AI models overestimated performance using high confidence scores, despite not achieving top validation accuracy.
  • High confidence scores did not consistently correlate with accurate classification in CEnR studies.

Conclusions:

  • Calibrated confidence scores can be misleading for assessing AI competency in CEnR.
  • Further research with larger datasets is needed to generalize findings and build expert trust in AI models.
  • Addressing bias and overfitting in AI models remains critical for reliable real-world decision-making.