{"id":"W4226305483","doi":"10.48550/arxiv.2112.15426","title":"Lunatic Stocks: Moon Phases as Irregular Sampling Features for Pattern Recognition in the Stock Markets","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Paranormal Experiences and Beliefs","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Full moon; New moon; Quarter (Canadian coin); Stock (firearms); Astrobiology; Stock market; Sampling (signal processing); Solar System; Geography; Geology; Computer science; Astronomy; Paleontology; Telecommunications; Physics; Archaeology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003757151,0.0003211584,0.0003533625,0.0002097747,0.000169295,0.000105347,0.0007156928,0.000377326,0.0009078555],"category_scores_gemma":[0.00007451631,0.0002996052,0.0002786604,0.0003244704,0.00009629183,0.0001457358,0.0002297186,0.0005847094,0.00003981792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001112437,"about_ca_system_score_gemma":0.00008463189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009228094,"about_ca_topic_score_gemma":0.0004203503,"domain_scores_codex":[0.9979915,0.0003392598,0.0002386036,0.000899983,0.0001048224,0.0004258231],"domain_scores_gemma":[0.9985192,0.0003934999,0.0002298302,0.0006709236,0.0001137281,0.00007279502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01347694,0.01486758,0.06945318,0.003741147,0.005556556,0.01381396,0.1449495,0.09961624,0.001455917,0.03592268,0.01865156,0.5784947],"study_design_scores_gemma":[0.03736443,0.00431829,0.4168753,0.007980701,0.004322662,0.0009800264,0.2318076,0.0831544,0.001783102,0.1449982,0.05339706,0.01301821],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9672952,0.0007184072,0.02284253,0.0000915019,0.0009508969,0.001027017,0.00009850731,0.00005408318,0.006921907],"genre_scores_gemma":[0.9958933,0.0001477005,0.0001185223,0.0004074095,0.0001625245,0.00004115311,0.0002700966,0.00002536319,0.002933957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5654765,"threshold_uncertainty_score":0.9999456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.160219587829625,"score_gpt":0.2849557270301235,"score_spread":0.1247361392004985,"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."}}