{"id":"W2983545835","doi":"10.3389/fpsyg.2019.02575","title":"Developing and Validating a Big-Store Multiple Errands Test","year":2019,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; Alberta Health Services; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Psychology; Test (biology); Reliability (semiconductor); Applied psychology; Set (abstract data type); Content validity; Ecological validity; Task (project management); Consistency (knowledge bases); Sample (material); Clinical psychology; Psychometrics; Cognition; Computer science; Psychiatry; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01270497,0.0005958292,0.0006601439,0.001320871,0.0006386531,0.001363072,0.001570774,0.001020121,0.002177902],"category_scores_gemma":[0.02623998,0.0005217164,0.0009312757,0.0006408363,0.0007843859,0.001627424,0.001886154,0.00144473,0.001408482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006006846,"about_ca_system_score_gemma":0.002066571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001943818,"about_ca_topic_score_gemma":0.0046696,"domain_scores_codex":[0.9938675,0.001794592,0.0008604868,0.0006135752,0.002532413,0.0003315416],"domain_scores_gemma":[0.9830108,0.00606676,0.001539181,0.001244099,0.00732471,0.0008144623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001117816,0.005866383,0.6223888,0.0005712463,0.0002428478,0.0005085268,0.009268397,0.003091812,0.01110427,0.002508563,0.01436669,0.3289647],"study_design_scores_gemma":[0.0005966662,0.01109788,0.9164415,0.0005219318,0.0001480826,0.001586673,0.00710739,0.01472055,0.01140595,0.003953874,0.03218601,0.0002335379],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9692402,0.0001831624,0.01725099,0.0003650005,0.0001549689,0.00435434,0.001175133,0.0002590636,0.007017269],"genre_scores_gemma":[0.8570846,0.0003589572,0.1226592,0.0006186903,0.00009134465,0.00894849,0.004295535,0.0001832755,0.005759826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01270497,"threshold_uncertainty_score":0.06719112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.14308399811872,"score_gpt":0.3806765767143545,"score_spread":0.2375925785956345,"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."}}