{"id":"W4292585402","doi":"10.48550/arxiv.1607.06187","title":"Exploring Differences in Interpretation of Words Essential in Medical\\n Expert-Patient Communication","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Jaccard index; Context (archaeology); Interpretation (philosophy); Quality of life (healthcare); Sample (material); Medicine; Psychology; Computer science; Artificial intelligence; Nursing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003429319,0.000183811,0.0003247217,0.0006281322,0.00003678802,0.00003421579,0.002223748,0.000159576,0.00001766774],"category_scores_gemma":[0.0001555987,0.0001860544,0.00007188691,0.0006143829,0.0001642697,0.0007210994,0.003661486,0.0005478312,0.000005961832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003509283,"about_ca_system_score_gemma":0.0001720107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004351136,"about_ca_topic_score_gemma":0.0002799494,"domain_scores_codex":[0.9980859,0.000373031,0.0003605157,0.0006361153,0.0002638808,0.0002805433],"domain_scores_gemma":[0.9981995,0.0003278508,0.0002329931,0.001032131,0.000107911,0.00009966204],"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":[0.0007358877,0.001695453,0.03180788,0.0007941319,0.0002169533,0.001359134,0.07517241,0.2348839,0.0008575424,0.08392013,0.00002342202,0.5685331],"study_design_scores_gemma":[0.000576412,0.00005328773,0.003636971,0.001604484,0.00000244658,0.000001468451,0.0005435906,0.9781494,0.0007259522,0.01443777,0.000007470174,0.0002607997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5136646,0.00006753094,0.485552,0.0001135141,0.0001831727,0.0001460326,0.000001685952,0.00003472851,0.0002367038],"genre_scores_gemma":[0.9965357,0.001185453,0.002196553,0.00001142813,0.00001559485,0.00001022979,0.000003526942,0.00000975009,0.0000317973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7432654,"threshold_uncertainty_score":0.7587079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1463618958385499,"score_gpt":0.2538733988732508,"score_spread":0.1075115030347008,"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."}}