{"id":"W7133367289","doi":"","title":"Data fusion and feature extraction of multi-modal time-series data using the Kendra framework: application in stress resilience training and moral ethical decision-making","year":2025,"lang":"en","type":"other","venue":"NPARC","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Resilience (materials science); Feature (linguistics); Sensor fusion; Stress (linguistics); Training set; Training (meteorology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002371745,0.0007724602,0.0008931005,0.001886367,0.0005993016,0.001814435,0.000575586,0.0006981589,0.00312494],"category_scores_gemma":[0.005360232,0.0002991882,0.001372118,0.002483891,0.0003324236,0.001348656,0.001083122,0.001056132,0.001276296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003481602,"about_ca_system_score_gemma":0.0007381127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004651223,"about_ca_topic_score_gemma":0.006178383,"domain_scores_codex":[0.9992622,0.0002567616,0.00006266827,0.0001797757,0.000169573,0.00006900403],"domain_scores_gemma":[0.9982567,0.0007707381,0.0001638424,0.0001967733,0.0005404425,0.00007143313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009242625,0.0006376065,0.01324664,0.0003885356,0.0005061228,0.0003392457,0.0005290926,0.05129834,0.02951887,0.004533768,0.009392564,0.8886848],"study_design_scores_gemma":[0.00003015738,0.0002701866,0.02788563,0.00009238415,0.0001859741,0.0001820444,0.0004903506,0.9414432,0.01476072,0.009334039,0.005203195,0.0001221512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1374188,0.001048403,0.8535357,0.0006144829,0.000351675,0.0001782558,0.001713902,0.002826441,0.002312391],"genre_scores_gemma":[0.5709173,0.0006614263,0.4225894,0.0000787114,0.0001328729,0.0001959619,0.002410038,0.0002763004,0.002737991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004651223,"threshold_uncertainty_score":0.01254314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07023722602272425,"score_gpt":0.3901368718842352,"score_spread":0.319899645861511,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}