{"id":"W6964386735","doi":"10.25976/h1bf-4i49","title":"RivTemp--National_Defence","year":2021,"lang":"en","type":"dataset","venue":"DataStream","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"General partnership; Variety (cybernetics); Foundation (evidence); Watershed; Oncorhynchus","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002837381,0.0005194004,0.0004954844,0.0002909784,0.0001209595,0.0002078347,0.001440113,0.0003173528,0.007683553],"category_scores_gemma":[0.0006587364,0.0005415509,0.0001343536,0.0006341572,0.0001477157,0.0002951426,0.0008875005,0.0006833971,0.1046472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001717744,"about_ca_system_score_gemma":0.0004400329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005698215,"about_ca_topic_score_gemma":0.0006406475,"domain_scores_codex":[0.9968411,0.0001599616,0.0004561096,0.0010005,0.001065386,0.0004768848],"domain_scores_gemma":[0.9963471,0.0001749816,0.0003296756,0.002734057,0.000204058,0.0002100891],"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":[0.000009750542,0.0002322593,0.000007143122,0.00008357376,0.0001052297,0.0003300881,0.000001278298,0.000006964867,0.00001700667,0.00002977078,0.9989723,0.0002046377],"study_design_scores_gemma":[0.0002264063,0.00001940905,0.0001218097,0.0001547545,0.0001487078,0.0001053461,0.00001047742,0.0000044305,0.00003145847,0.00003981514,0.9986399,0.0004974815],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000004092737,0.0003806734,0.000003471649,0.00004575746,0.0006176306,0.000189501,0.9983547,0.0001010312,0.0003031209],"genre_scores_gemma":[0.000001646557,0.0001794681,0.000345499,0.0002487536,0.0005782921,0.00006471195,0.9982658,0.00007418628,0.0002416548],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09696361,"threshold_uncertainty_score":0.9997036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02274580694292508,"score_gpt":0.2938181211232099,"score_spread":0.2710723141802848,"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."}}