{"id":"W2162806193","doi":"10.5539/jsd.v5n12p40","title":"Identification of Physical Transportation Infrastructure Vulnerable to Sea Level Rise","year":2012,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Storm surge; Transportation infrastructure; Terrain; Identification (biology); Environmental resource management; Sea level; Geographic information system; Transport engineering; Environmental planning; Environmental science; Storm; Geography; Meteorology; Physical geography; Engineering; Remote sensing; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005367206,0.0001168457,0.000258648,0.0002453778,0.00007677056,0.00002022728,0.0001400427,0.00006896864,0.00004156687],"category_scores_gemma":[0.00008722578,0.00009460565,0.0001029617,0.0004142694,0.0000205968,0.0004393677,0.000007744998,0.0002755289,0.000004510541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003103415,"about_ca_system_score_gemma":0.00008918319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001552475,"about_ca_topic_score_gemma":0.000003825269,"domain_scores_codex":[0.9987475,0.00002744543,0.0005884849,0.00006301432,0.000318202,0.0002553384],"domain_scores_gemma":[0.9990721,0.00003842461,0.0001582745,0.00009347712,0.0005013886,0.0001363596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001653796,0.0002961232,0.01949457,0.001784995,0.0009188938,0.00003712959,0.07123284,0.7791519,0.09428637,0.002939264,0.006999263,0.0226933],"study_design_scores_gemma":[0.0004708199,0.0000767271,0.4729463,0.00008286592,0.0001798062,0.00001998873,0.01873334,0.002708181,0.4869509,0.001259731,0.01620425,0.0003671312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886519,0.000169542,0.01072393,0.0000803955,0.0001798742,0.0001042302,0.000005808512,0.00001306372,0.00007121939],"genre_scores_gemma":[0.9968597,0.0000148522,0.00266153,0.00001104308,0.0001130282,0.00000441596,0.000005561928,0.00001066816,0.0003192035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7764437,"threshold_uncertainty_score":0.3857907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008785247892510057,"score_gpt":0.2342943393870541,"score_spread":0.2255090914945441,"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."}}