{"id":"W2611879961","doi":"","title":"Predicting Ten Thousand Bits from Ten Thousand Inputs","year":2006,"lang":"en","type":"report","venue":"UCL Discovery (University College London)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Genetic programming; Task (project management); Code (set theory); Computer science; Series (stratigraphy); Evolutionary computation; Binary number; Computation; Artificial intelligence; Machine learning; Algorithm; Mathematics; Arithmetic; Programming language; Biology; Set (abstract data type); Engineering","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":[0.0003583652,0.0006060151,0.0007734927,0.0005420602,0.001188245,0.0002915367,0.002263085,0.0005648584,0.00004878762],"category_scores_gemma":[0.00004623966,0.0006663704,0.0003856959,0.001647589,0.0001970897,0.00254778,0.001594984,0.0007321116,0.00009113705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008379249,"about_ca_system_score_gemma":0.002156213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00548373,"about_ca_topic_score_gemma":0.001142157,"domain_scores_codex":[0.9958426,0.0001481472,0.0004661843,0.001508159,0.001350145,0.0006847819],"domain_scores_gemma":[0.9969196,0.0002938978,0.0006313638,0.001464608,0.0004424788,0.000248085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001151113,0.001445993,0.04312097,0.0002990075,0.001099385,0.00268682,0.0007024562,0.001658873,0.0002846501,0.04190282,0.8992866,0.007397337],"study_design_scores_gemma":[0.002306481,0.0001817428,0.1070663,0.0005649654,0.0004752983,0.0002306797,0.0003315967,0.03665115,0.00008557775,0.002498989,0.8474883,0.00211897],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.0897878,0.003744116,0.5761297,0.004936608,0.004266682,0.002926243,0.03093567,0.002022893,0.2852503],"genre_scores_gemma":[0.2605464,0.004262546,0.0644022,0.000548994,0.004811928,0.00004870648,0.004578924,0.0002849769,0.6605152],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.5117275,"threshold_uncertainty_score":0.9995788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01150905070360123,"score_gpt":0.2151366890912338,"score_spread":0.2036276383876325,"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."}}