{"id":"W1524279467","doi":"10.2139/ssrn.2566401","title":"Public Transit Data Through an Intellectual Property Lens: Lessons About Open Data","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Intellectual property; Public transport; Transit (satellite); Open data; Business; Lens (geology); Data science; Law and economics; Computer science; Transport engineering; Political science; Law; Engineering; Sociology; Optics; Physics","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.01982221,0.0005688114,0.0007500505,0.004683596,0.005023359,0.02900679,0.002978099,0.007309833,0.01364717],"category_scores_gemma":[0.06436353,0.0004732512,0.0008890408,0.007046091,0.03538312,0.07731267,0.008964371,0.01313129,0.001339301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007330081,"about_ca_system_score_gemma":0.008781064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0102862,"about_ca_topic_score_gemma":0.007830846,"domain_scores_codex":[0.9899624,0.005671919,0.0004590334,0.0008940344,0.002425239,0.0005872375],"domain_scores_gemma":[0.8853962,0.08926798,0.003151547,0.01091564,0.008310365,0.002958347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.000006138173,0.000007965242,0.0002724674,0.0000314322,0.000003364645,0.00003496884,0.001006099,0.0001748389,0.00001757132,0.9888937,0.004236698,0.005314693],"study_design_scores_gemma":[0.000006662097,0.000007688934,0.0002385167,0.000256153,0.000005434931,0.00007557454,0.003860825,0.0007892936,0.0001162138,0.917158,0.07747315,0.0000124274],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02244151,0.01215344,0.1165485,0.5461972,0.003582554,0.00007922603,0.0009834684,0.0002848512,0.2977293],"genre_scores_gemma":[0.9013429,0.01608238,0.02925995,0.02222158,0.008220637,0.000190278,0.0004780852,0.0005716421,0.0216325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9970219,"threshold_uncertainty_score":0.1048311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3719693665461619,"score_gpt":0.3558118026463493,"score_spread":0.01615756389981265,"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."}}