{"id":"W4255144882","doi":"10.1515/iupac.68.0045","title":"Collision Frequency Factor","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Thermal and Kinetic Analysis","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Glossary; Terminology; Field (mathematics); Computer science; Collision; 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":[0.001652828,0.002552081,0.002104398,0.006052142,0.001106709,0.002825829,0.002945576,0.001346316,0.1021971],"category_scores_gemma":[0.01133405,0.0008635959,0.00187125,0.006960433,0.0004231191,0.00265207,0.001816749,0.002362735,0.1355023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001459805,"about_ca_system_score_gemma":0.002083868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01384776,"about_ca_topic_score_gemma":0.02220778,"domain_scores_codex":[0.9976104,0.000252868,0.000468132,0.0009120862,0.0005897433,0.0001667498],"domain_scores_gemma":[0.9962519,0.001123638,0.0004227934,0.001131892,0.0009430257,0.0001268222],"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":[0.0002251924,0.00005601687,0.002433784,0.001929235,0.0001104634,0.00003693827,0.00003355207,0.0005243199,0.0006559205,0.0008109481,0.9757404,0.01744325],"study_design_scores_gemma":[0.0002357435,0.00004879757,0.008493175,0.0004812968,0.0001037414,0.0002034875,0.00006386267,0.000747254,0.001802447,0.003399375,0.9843274,0.00009348575],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003016066,0.0002983997,0.0005829405,0.00004563786,0.00006925859,0.00004581709,0.9962354,0.001119638,0.001301337],"genre_scores_gemma":[0.0007156655,0.0002103234,0.001221744,0.00007184723,0.00001619049,0.0001836551,0.9962948,0.00020036,0.00108543],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1021971,"threshold_uncertainty_score":0.3418835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01372917843145385,"score_gpt":0.370354310138149,"score_spread":0.3566251317066952,"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."}}