{"id":"W3167041284","doi":"10.1371/journal.pone.0253057","title":"EA3: A softmax algorithm for evidence appraisal aggregation","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medizinische Universität Wien; European Research Council; Universität Wien; University of Toronto; Deutsche Forschungsgemeinschaft","keywords":"Softmax function; Inference; Function (biology); Causal inference; Bayesian probability; Computer science; Aggregate (composite); Quality (philosophy); Risk analysis (engineering); Artificial intelligence; Machine learning; Data science; Management science; Medicine; Econometrics; Mathematics; Economics; Artificial neural network; Epistemology","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":[0.01586127,0.001528826,0.002075796,0.004784817,0.0009561072,0.003440552,0.002434637,0.002560139,0.008447128],"category_scores_gemma":[0.04690954,0.0009108082,0.002414918,0.003899504,0.001080464,0.003547457,0.004455707,0.0036674,0.002096076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001697755,"about_ca_system_score_gemma":0.00430524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002701679,"about_ca_topic_score_gemma":0.003626043,"domain_scores_codex":[0.9931409,0.003755976,0.0009193151,0.0007260822,0.00118814,0.0002695494],"domain_scores_gemma":[0.9797117,0.01551953,0.001065837,0.001157243,0.002213414,0.00033228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004863778,0.0001867747,0.002454175,0.0007615731,0.0006155901,0.0002169434,0.0004860917,0.1695516,0.002368035,0.04642108,0.0118378,0.7646141],"study_design_scores_gemma":[0.0001344842,0.0001467657,0.0007945401,0.0002139037,0.0001439697,0.0001200407,0.0001000129,0.8352284,0.002664459,0.1515036,0.008902323,0.00004741374],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002914564,0.0004811444,0.9938462,0.0005036437,0.00006334922,0.00021508,0.0001861403,0.0007207414,0.001069129],"genre_scores_gemma":[0.07915994,0.000425447,0.9164832,0.0004697219,0.0001564092,0.0008005001,0.0005635892,0.0002147989,0.001726381],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01586127,"threshold_uncertainty_score":0.0838834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3323084250666219,"score_gpt":0.4230801135302618,"score_spread":0.09077168846363981,"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."}}