## Dataset Introduction OmniThoughtV is a large-scale multimodal long-chain-of-thought dataset distilled from the [FineVision](https://huggingface.co/datasets/HuggingFaceM4/FineVision) dataset using
BackgroundTypically, a two-phase (double) sampling strategy is employed when classifications are subject to error and there is a gold standard (perfect) classifier available. Two-phase sampling involv
Statistical significance of differences in performance of the different classifiers at loss and recovery of consciousness (LOC and ROC respectively). Classifiers: linear (SVML) and nonlinear (SVMNL) S
AUC results for low p/n data. Low p/n results for prediction accuracy using AUC as the performance metric for non-cross-validation results, 10-fold cross-validation and stratified 10-fold cross-valida
Class-specific producer’s accuracies (PA), user’s accuracies (UA), and overall accuracies (OA) (%) for the different classifiers (Manas, training sample number = 4,000).
