{"id":"W4353100324","doi":"10.54097/hset.v34i.5494","title":"Predicting Titanic Survivors by Using Machine Learning","year":2023,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cruise; Artificial intelligence; Machine learning; Task (project management); Test (biology); Competition (biology); Hull; Point (geometry); Computer science; Test set; Oceanography; History; Engineering; Geology; Mathematics; Ecology; Paleontology","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":[0.001121815,0.000570379,0.0003345257,0.002215713,0.0002867245,0.0007215436,0.0004450478,0.0005418233,0.001252305],"category_scores_gemma":[0.005471716,0.0001068765,0.0004241,0.0008321477,0.000205721,0.0005535355,0.0004354622,0.0005380394,0.0006769706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004106524,"about_ca_system_score_gemma":0.0004419825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00666865,"about_ca_topic_score_gemma":0.007735663,"domain_scores_codex":[0.9995201,0.0001438326,0.0000444409,0.0000960996,0.0001136464,0.00008189632],"domain_scores_gemma":[0.9975215,0.001501857,0.0003152416,0.0001110907,0.000395619,0.0001546672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004199804,0.0004817533,0.7275519,0.0001040216,0.0001745015,0.0002834533,0.000418367,0.09264424,0.001302751,0.0005036003,0.005108237,0.1710071],"study_design_scores_gemma":[0.00002619655,0.001029915,0.2582521,0.00008972992,0.00009360925,0.0003923228,0.001561969,0.728273,0.003244677,0.002622524,0.004349058,0.00006481163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788847,0.0003318539,0.01587787,0.0005690184,0.00008425613,0.00008297993,0.001398013,0.0002474358,0.002523873],"genre_scores_gemma":[0.9926673,0.000120586,0.004590882,0.00005525129,0.00003097928,0.00003186877,0.001420478,0.000007646308,0.001075095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00666865,"threshold_uncertainty_score":0.01325971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05033633436373746,"score_gpt":0.3774473887674155,"score_spread":0.327111054403678,"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."}}