{"id":"W4392588996","doi":"10.31449/inf.v48i6.5234","title":"Machine Learning Algorithms for Transportation Mode Prediction: A Comparative Analysis","year":2024,"lang":"en","type":"article","venue":"Informatica","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Applied Science Private University","keywords":"Computer science; Mode (computer interface); Machine learning; Algorithm; Artificial intelligence; Human–computer interaction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01154882,0.001634009,0.001350157,0.008019296,0.000575716,0.002130904,0.001257636,0.00149662,0.001200588],"category_scores_gemma":[0.02932903,0.000314954,0.001375639,0.005834023,0.0003849091,0.003560746,0.0009321425,0.001384015,0.0005290567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001456308,"about_ca_system_score_gemma":0.0009905749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007915165,"about_ca_topic_score_gemma":0.00472415,"domain_scores_codex":[0.9928393,0.002652518,0.0007902348,0.001105681,0.002352396,0.0002596813],"domain_scores_gemma":[0.9665001,0.02709358,0.0008396997,0.001540562,0.003794858,0.0002311798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000718711,0.0004372023,0.06093589,0.001342828,0.001505299,0.0001656586,0.0002274415,0.2118959,0.0009089951,0.007235281,0.007471381,0.7071553],"study_design_scores_gemma":[0.00004162521,0.0005714879,0.02504772,0.0004257237,0.0004371228,0.000271881,0.0003485148,0.9552655,0.002633426,0.006486809,0.008394358,0.00007578841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4722535,0.1290329,0.3586796,0.004085308,0.001176363,0.0004446154,0.003922754,0.003632555,0.02677242],"genre_scores_gemma":[0.8437978,0.01761191,0.1325848,0.0002738079,0.0002992669,0.0001453074,0.003711266,0.0001999086,0.001376044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01154882,"threshold_uncertainty_score":0.0610767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01641271836811846,"score_gpt":0.2679279128554686,"score_spread":0.2515151944873501,"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."}}