{"id":"W4254210645","doi":"10.1515/iupac.68.0179","title":"Strong Collision","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Glossary; Terminology; Field (mathematics); Collision; Computer science; Epistemology; Linguistics; Philosophy; Mathematics; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009231054,0.003441314,0.001810567,0.003337157,0.001565222,0.002957245,0.004871623,0.001802632,0.06873757],"category_scores_gemma":[0.003995808,0.0007189644,0.002249363,0.004207601,0.0004716195,0.002768824,0.002523539,0.002677244,0.1286652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001387533,"about_ca_system_score_gemma":0.001988154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01508247,"about_ca_topic_score_gemma":0.04205089,"domain_scores_codex":[0.998517,0.0001696783,0.00018424,0.0004535554,0.0004470367,0.0002284956],"domain_scores_gemma":[0.9985927,0.0002038644,0.0001106775,0.0006161365,0.0003584206,0.0001181668],"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":[0.0001063094,0.00004840646,0.0009143948,0.0004514283,0.00003235482,0.0000319704,0.00001377626,0.0004781113,0.0001791435,0.0006035417,0.9882541,0.008886353],"study_design_scores_gemma":[0.0001948294,0.00007395808,0.004950797,0.0003474883,0.00004976465,0.0003637062,0.0001339014,0.002945215,0.001369501,0.003714678,0.9857821,0.00007408876],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001168772,0.000555299,0.001244878,0.0001963775,0.0003303898,0.0001141052,0.9860124,0.003325656,0.007052097],"genre_scores_gemma":[0.001231229,0.0001962081,0.001458871,0.0001230256,0.00002946093,0.0001477769,0.9942299,0.0001815164,0.002401962],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06873757,"threshold_uncertainty_score":0.2299501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01389930445408946,"score_gpt":0.3941379558156465,"score_spread":0.3802386513615571,"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."}}