{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001291938,0.0001291595,0.0001945065,0.001070274,0.0008777793,0.00001199455,0.000295107,0.0002381938,0.00001187695],"category_scores_gemma":[0.001255737,0.0001189204,0.0000107092,0.003646823,0.0002652179,0.0001609022,0.0002602371,0.0008333183,0.0001896978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002124267,"about_ca_system_score_gemma":0.0001436372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001858872,"about_ca_topic_score_gemma":0.0009868662,"domain_scores_codex":[0.998138,0.00004795077,0.000391037,0.0004000312,0.0002128098,0.0008101979],"domain_scores_gemma":[0.9991621,0.0003672526,0.00007989597,0.0002073857,0.0000887214,0.00009459079],"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":[0.000004488854,0.0000129197,0.8558407,0.000134141,0.000004401011,0.000032258,0.002291574,0.005029081,0.1036698,0.03152137,0.00006450785,0.001394831],"study_design_scores_gemma":[0.000101833,0.00006281227,0.001890177,0.0002991205,0.000003351462,0.000007438856,0.002791623,0.9751788,0.008055015,0.0006102012,0.01076207,0.0002375907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995969,0.0002982916,0.0001654595,0.001405434,0.000844001,0.0002136222,0.000005227668,0.0009756002,0.0001233451],"genre_scores_gemma":[0.9990343,0.0002351146,0.0004259686,0.00001499334,0.00006094877,0.00003298775,0.000002250717,0.00002104342,0.0001724294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9701497,"threshold_uncertainty_score":0.6751257,"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."}}