{"id":"W4384074186","doi":"10.1109/msr59073.2023.00005","title":"Message from the MSR 2023 General and Program Co-Chairs","year":2023,"lang":"en","type":"article","venue":"","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Concordia University","keywords":"Computer science; Environmental science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046704,0.0008772183,0.0009466318,0.0005760784,0.002384635,0.003600399,0.001472515,0.01930319,0.02506216],"category_scores_gemma":[0.02021826,0.0004626005,0.0008986913,0.0005273884,0.001326862,0.002262877,0.00204026,0.02350272,0.02394927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002547587,"about_ca_system_score_gemma":0.007153666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005433206,"about_ca_topic_score_gemma":0.01032907,"domain_scores_codex":[0.9968024,0.0003717494,0.0001465309,0.0003787765,0.001995067,0.0003053705],"domain_scores_gemma":[0.9870941,0.002248788,0.0007678545,0.0003789392,0.00543527,0.004075211],"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":[0.00003714416,0.00001298413,0.0001096045,0.00001792363,0.000003473979,0.00003935027,0.00001393556,0.00001279123,0.0000867346,0.0002597747,0.9974776,0.001928733],"study_design_scores_gemma":[0.00005000178,0.00007048961,0.001447988,0.0001055878,0.00001391032,0.000159702,0.000174807,0.000149372,0.0003179184,0.0008012525,0.9966801,0.00002886065],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"editorial","genre_scores_codex":[0.0004137492,0.0009897344,0.00028192,0.922865,0.07124147,0.0000306561,0.0004016836,0.000184354,0.003591455],"genre_scores_gemma":[0.003292114,0.0007079559,0.0005529642,0.9214473,0.02888454,0.00007844301,0.0002887873,0.00009832054,0.04464969],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.02506216,"threshold_uncertainty_score":0.08384132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.160056616077477,"score_gpt":0.4317559820678635,"score_spread":0.2716993659903865,"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."}}