{"id":"W6968161901","doi":"10.5281/zenodo.15538160","title":"Wonkyconn Smoke Test Data","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Technologies and Applied Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Smoke; Test (biology); Test data; Cigarette smoke","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":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.0005648994,0.0002849723,0.0002764152,0.000365631,0.002233275,0.001720853,0.01569929,0.0002059167,0.0008665257],"category_scores_gemma":[0.001700993,0.0003013503,0.00004722892,0.001145567,0.0001544731,0.0003877053,0.03320785,0.0008329387,0.001382402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001319303,"about_ca_system_score_gemma":0.00001246631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000217925,"about_ca_topic_score_gemma":3.501784e-7,"domain_scores_codex":[0.9973416,0.0001083272,0.0003581978,0.001217671,0.0004276286,0.0005465934],"domain_scores_gemma":[0.9947445,0.0001180606,0.0002320621,0.004489601,0.0002993468,0.0001164674],"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":[0.000002135973,0.00006724203,4.834153e-8,0.00006535549,0.00002004964,0.00002322452,0.00000966114,0.00001782054,0.0000157904,0.003742516,0.8525162,0.1435199],"study_design_scores_gemma":[0.0001944999,0.00008791815,0.000003418157,0.00008001298,0.00001322191,0.00005225313,0.00002505279,0.001741299,0.00003259212,0.0007944535,0.9966772,0.000298051],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000002808604,0.0001688174,0.07340495,0.0009861935,0.0002712407,0.0003936388,0.9119515,0.002422649,0.01039816],"genre_scores_gemma":[0.00013518,0.0003364564,0.003805047,0.0002966004,0.0001611024,3.870927e-8,0.9946543,0.0003245417,0.0002867233],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.144161,"threshold_uncertainty_score":0.9999439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0588817202467742,"score_gpt":0.2832229448573055,"score_spread":0.2243412246105313,"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."}}