{"id":"W2057678386","doi":"10.3934/nhm.2015.10.1","title":"Transferability of collective transportation line networks from a topological and passenger demand perspective","year":2015,"lang":"en","type":"article","venue":"Networks and Heterogeneous Media","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Junta de Andalucía; Ministerio de Economía y Competitividad","keywords":"Transferability; Linearization; Perspective (graphical); Computer science; Line (geometry); Complex network; Topology (electrical circuits); Mathematics; Physics; Artificial intelligence; Combinatorics; Machine learning; Geometry; Nonlinear system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002076425,0.0002087624,0.0004941059,0.00004052561,0.00008000241,0.00002815849,0.00007804613,0.000119858,0.00008294341],"category_scores_gemma":[0.00000669879,0.0001804548,0.0001096149,0.0001671771,0.0002042389,0.00005351276,0.00001913837,0.0002128059,1.815821e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003592229,"about_ca_system_score_gemma":0.00003433294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007081538,"about_ca_topic_score_gemma":0.0008252314,"domain_scores_codex":[0.9987881,0.000108535,0.0003328015,0.0003954342,0.0001401862,0.0002349727],"domain_scores_gemma":[0.9991087,0.0002485593,0.00009647606,0.0001653118,0.0001772555,0.0002037381],"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":[0.001249218,0.0008119905,0.8055155,0.00001701753,0.001388179,0.00002958611,0.01390246,0.1253771,0.0001601528,0.01036307,0.0006945081,0.04049127],"study_design_scores_gemma":[0.003798128,0.001187366,0.1784054,0.0001116144,0.001123206,0.000007563031,0.003397461,0.6555674,0.001176912,0.1536737,0.0004118228,0.001139463],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8569198,0.002637154,0.1398252,0.00007118934,0.00006591955,0.0002419998,0.00004255797,0.00003604511,0.0001600983],"genre_scores_gemma":[0.9984374,0.0001632308,0.0006962835,0.0000319558,0.0005215179,0.00003293174,0.00009417697,0.00001543857,0.000007068785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6271101,"threshold_uncertainty_score":0.7358734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02215195745040255,"score_gpt":0.2609142888967821,"score_spread":0.2387623314463796,"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."}}