{"id":"W7034701448","doi":"","title":"Turvallisuus ennen kaikkea? : Suomen lainsäädännöllinen varautuminen maahanmuuton välineellistämiseen hybridiuhkana","year":2023,"lang":"fi","type":"other","venue":"UTUPub (University of Turku)","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Range (aeronautics); Quarter (Canadian coin); Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007883474,0.0004306152,0.0004922872,0.0003657929,0.001752513,0.005177035,0.0006383933,0.001189134,0.01937187],"category_scores_gemma":[0.0006686935,0.0003040463,0.0005354811,0.000506571,0.001039064,0.002648363,0.001675171,0.002222017,0.005486081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00188575,"about_ca_system_score_gemma":0.001599239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004505475,"about_ca_topic_score_gemma":0.01047843,"domain_scores_codex":[0.9994583,0.0001005592,0.00001786936,0.0001971838,0.0001116992,0.000114384],"domain_scores_gemma":[0.9996493,0.00008682866,0.00004878261,0.00003860995,0.00009382956,0.00008277508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002047595,0.000563197,0.02937035,0.002827975,0.0002234914,0.004401946,0.02957751,0.001145978,0.3664441,0.08561189,0.02822979,0.4495561],"study_design_scores_gemma":[0.00001959017,0.0003115223,0.02281559,0.000493046,0.0001072707,0.001190354,0.01536803,0.0004388454,0.04430744,0.01169804,0.9031465,0.0001038296],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5904657,0.04764663,0.01893371,0.01987728,0.002900477,0.000143006,0.001355757,0.0008378762,0.3178396],"genre_scores_gemma":[0.698751,0.01223191,0.01167392,0.003230103,0.0002800302,0.0001138153,0.0009975139,0.0005244334,0.2721973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01937187,"threshold_uncertainty_score":0.06480533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01387432154621178,"score_gpt":0.1707998556090878,"score_spread":0.156925534062876,"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."}}