{"id":"W2129248434","doi":"10.1109/eicccc.2006.277248","title":"Adaptation Options for Infrastructure Under Changing Climate Conditions","year":2006,"lang":"en","type":"article","venue":"","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Impact","funders":"","keywords":"Adaptation (eye); Prioritization; Climate change; Risk analysis (engineering); Computer science; Workaround; Environmental resource management; Enforcement; Contingency plan; Climate change adaptation; Critical infrastructure; Environmental planning; Business; Environmental science; Process management; Computer security","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":[0.001425588,0.0005229391,0.0002460531,0.0008107202,0.0008537108,0.001845157,0.00122142,0.001716762,0.01518239],"category_scores_gemma":[0.004344118,0.0002892924,0.0006826541,0.0006067149,0.0009117759,0.002571997,0.002033732,0.001141989,0.001146021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009377621,"about_ca_system_score_gemma":0.0008106114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002348595,"about_ca_topic_score_gemma":0.003983339,"domain_scores_codex":[0.9989536,0.0003793348,0.00004376024,0.0001067224,0.0002580454,0.0002585258],"domain_scores_gemma":[0.9989575,0.0003487568,0.0001412489,0.0001458857,0.0002548252,0.0001517709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001383673,0.00008848053,0.004553858,0.0001516596,0.00006442132,0.001092898,0.0005209088,0.6013542,0.004997156,0.3065901,0.01018736,0.0702606],"study_design_scores_gemma":[0.00005338455,0.0002394852,0.009626497,0.0001381563,0.00008515936,0.0008947161,0.002335545,0.3951272,0.002094732,0.5006166,0.08863599,0.0001525645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2870511,0.001892931,0.3491155,0.01241543,0.0004989821,0.0003282306,0.0008479031,0.0007857746,0.347064],"genre_scores_gemma":[0.968924,0.0006562648,0.01287009,0.0002471814,0.00007342838,0.000164779,0.0002033307,0.00007171811,0.01678914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01518239,"threshold_uncertainty_score":0.05079013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008230551308484584,"score_gpt":0.2438497779566033,"score_spread":0.2356192266481187,"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."}}