{"id":"W4388757087","doi":"10.1109/lsens.2023.3333657","title":"IEEE Sensors Council Information","year":2023,"lang":"en","type":"article","venue":"IEEE Sensors Letters","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Information retrieval","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008337113,0.000186515,0.0003342989,0.000325728,0.000211856,0.0001537246,0.0002100917,0.0000746525,0.0009017697],"category_scores_gemma":[0.0001078835,0.0002209729,0.0001511644,0.0004384208,0.0001339707,0.0006357512,0.00003517955,0.0001241141,0.1550662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003237293,"about_ca_system_score_gemma":0.00002134964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001374793,"about_ca_topic_score_gemma":0.000005462831,"domain_scores_codex":[0.9984732,0.00001075914,0.0005680392,0.0003569467,0.0001040939,0.000486937],"domain_scores_gemma":[0.9991915,0.0000600122,0.0002627052,0.0003435856,0.00005334156,0.00008890586],"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":[0.000009455829,0.000008037552,0.0006887076,0.0000181664,0.00006734282,0.000009455922,0.002344206,0.008763892,0.0001906336,0.001358696,0.9864286,0.0001128669],"study_design_scores_gemma":[0.0005940642,0.00001381928,0.00174058,0.00000945449,0.000007793623,0.000004654011,0.0008780464,0.00660712,0.0002985315,0.0003710834,0.9890581,0.00041677],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8679878,0.00003255151,0.00008331465,0.003194122,0.009523084,0.0001981755,0.0006032889,0.0002397251,0.1181379],"genre_scores_gemma":[0.7652723,0.0003600619,0.0002759344,0.008391126,0.0007483159,0.00004245048,0.0000691606,0.00006048886,0.2247802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1541645,"threshold_uncertainty_score":0.9873747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06568098960133949,"score_gpt":0.1987921485464502,"score_spread":0.1331111589451107,"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."}}