{"id":"W2296186174","doi":"10.48550/arxiv.1512.06657","title":"Inside the clustering window for random linear equations","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Cluster analysis; Mathematics; Cluster (spacecraft); Partition (number theory); Infinity; Combinatorics; Random variable; Statistical physics; Random graph; Discrete mathematics; Physics; Mathematical analysis; Computer science; Statistics; Graph","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003780343,0.0001687582,0.0001808625,0.0001414905,0.0002460877,0.000131219,0.0008020156,0.000143463,0.00001612124],"category_scores_gemma":[0.0001557932,0.0001570599,0.0001535142,0.0002965874,0.000072165,0.0002852503,0.00075766,0.0002692981,0.00002754996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00013929,"about_ca_system_score_gemma":0.0003081606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005902284,"about_ca_topic_score_gemma":0.0002039429,"domain_scores_codex":[0.9989817,0.0001043212,0.000164758,0.0004936837,0.00007121261,0.0001843444],"domain_scores_gemma":[0.998423,0.0003151959,0.0001849695,0.0006963882,0.0002855716,0.00009488021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002743467,0.0000131504,0.000124617,0.00001407151,0.00003606842,0.000005936995,0.000289781,0.9561636,0.000005016895,0.04080273,0.000216938,0.002300661],"study_design_scores_gemma":[0.001073273,0.00001741036,0.0001325844,0.00002450116,0.00003963347,0.00000230287,0.00007044169,0.9819205,0.00001548541,0.01535609,0.00116483,0.0001829858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002928487,0.00001943257,0.9930999,0.0005998395,0.0007861442,0.0006100939,0.00001581063,0.0001787704,0.001761538],"genre_scores_gemma":[0.9881387,0.0000370687,0.01063773,0.0001780217,0.0001061863,0.000004252979,0.00002549157,0.00001081887,0.0008617439],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9852102,"threshold_uncertainty_score":0.6404719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1201742946619195,"score_gpt":0.2151973530128497,"score_spread":0.09502305835093028,"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."}}