{"id":"W4283790178","doi":"10.3390/e24070928","title":"From Random Numbers to Random Objects","year":2022,"lang":"en","type":"article","venue":"Entropy","topic":"DNA and Biological Computing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Random permutation; Random number generation; Computer science; Random function; Cryptography; Random field; Theoretical computer science; Random compact set; Object (grammar); Random graph; Mathematics; Algorithm; Discrete mathematics; Artificial intelligence; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001477569,0.00009916808,0.0001336176,0.00001568926,0.0001580347,0.00001777607,0.0002056774,0.00003916496,0.0004113442],"category_scores_gemma":[0.00009538957,0.00008312447,0.000101425,0.00007425504,0.00001737473,7.841761e-7,0.0003175353,0.00009033505,0.000045065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001471185,"about_ca_system_score_gemma":0.0000196125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006381245,"about_ca_topic_score_gemma":0.00000495923,"domain_scores_codex":[0.9991148,0.0001338921,0.0001256657,0.0002977791,0.0001165113,0.0002113942],"domain_scores_gemma":[0.9996498,0.00003240662,0.00003624357,0.0001838851,0.00001480171,0.00008285994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001504574,0.00006108387,0.001827502,0.000001351403,0.00005336863,0.00001457472,0.0001414617,0.0005747254,0.9763208,0.00008069064,0.01733888,0.002080979],"study_design_scores_gemma":[0.01429687,0.001199005,0.00185065,0.000007589269,0.00004074607,0.00001789004,0.0007926759,0.0005148011,0.2556379,0.0008522021,0.724134,0.0006557177],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928033,0.0002856903,0.003711713,0.0002979513,0.0004744143,0.0001886099,0.00003212834,0.00002405479,0.002182081],"genre_scores_gemma":[0.9958265,0.000007351895,0.0008526153,0.00156636,0.0005616281,0.00003223198,0.0001594989,0.000009567065,0.0009842423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.720683,"threshold_uncertainty_score":0.4503931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006907217697714202,"score_gpt":0.2332522288866146,"score_spread":0.2263450111889004,"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."}}