{"id":"W2955892276","doi":"10.1371/journal.pone.0218966","title":"Intrinsic dimensionality of human behavioral activity data","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Curse of dimensionality; Computer science; Data science; Dimension (graph theory); Intrinsic dimension; Scope (computer science); Data mining; Correlation; Dimensionality reduction; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008011783,0.00005093259,0.000186821,0.00004107683,0.0001909148,0.00001687617,0.0003561855,0.0000531927,0.001866111],"category_scores_gemma":[0.0001067034,0.00005300682,0.00003657885,0.0001950278,0.0001865485,0.0002035753,0.0001076475,0.0000970616,0.0001018029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005013455,"about_ca_system_score_gemma":0.0001026187,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01850873,"about_ca_topic_score_gemma":0.01550337,"domain_scores_codex":[0.9988251,0.0001971216,0.0001379397,0.0002276713,0.0004874962,0.0001246756],"domain_scores_gemma":[0.9989712,0.00009527524,0.00008613827,0.0006539361,0.0001362055,0.00005725586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006118536,0.02001162,0.7856336,0.0002568357,0.0004656974,0.000001973209,0.009010213,0.000008547737,0.1508891,0.0163169,0.0003335254,0.0170108],"study_design_scores_gemma":[0.001366309,0.0005195758,0.9291033,0.0003403646,0.00151518,7.157177e-8,0.004164171,0.001184991,0.05292916,0.006812034,0.001167985,0.0008968703],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974539,0.00001348701,0.000006866936,0.0003647194,0.00001615968,0.0001683638,0.00003455361,0.00002616361,0.00191573],"genre_scores_gemma":[0.9989325,0.000004855623,0.00007553503,0.00002764263,0.00006159789,0.000003422538,0.00006795194,0.00000356865,0.0008228658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1434697,"threshold_uncertainty_score":0.9990463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1925195128538531,"score_gpt":0.3675663505868163,"score_spread":0.1750468377329632,"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."}}