{"id":"W2174237385","doi":"10.4043/23850-ms","title":"Best Practice in Arctic Development Concept Selection - How to Avoid the Traps","year":2012,"lang":"en","type":"article","venue":"OTC Arctic Technology Conference","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Intecsea (Canada)","funders":"","keywords":"Computer science; Arctic; Selection (genetic algorithm); Process (computing); Key (lock); Set (abstract data type); The arctic; Field (mathematics); Development plan; Operations research; Risk analysis (engineering); Process management; Environmental resource management; Environmental science; Engineering; Geology; Business; Civil engineering; Artificial intelligence; Oceanography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.1315483,0.002287531,0.001384333,0.007683077,0.01012025,0.02007953,0.009235259,0.007000533,0.01321721],"category_scores_gemma":[0.1424576,0.001654452,0.001461763,0.004629793,0.01362109,0.01703474,0.01380033,0.007621599,0.007492535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01131849,"about_ca_system_score_gemma":0.03891204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007688623,"about_ca_topic_score_gemma":0.01606666,"domain_scores_codex":[0.8220656,0.1239218,0.008684754,0.00726355,0.03272589,0.00533834],"domain_scores_gemma":[0.8189147,0.08808076,0.0131347,0.02134297,0.04753298,0.01099391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001942834,0.001166697,0.007092416,0.005181752,0.0001423121,0.001679957,0.05467416,0.0108117,0.00423132,0.1009912,0.08216987,0.7316644],"study_design_scores_gemma":[0.0001861744,0.0007035332,0.005227922,0.01778979,0.00008880471,0.001728972,0.07643091,0.01056412,0.006362348,0.2480806,0.6323642,0.0004727605],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.04918794,0.01696044,0.6172782,0.171713,0.002641024,0.004091939,0.0002494568,0.003100606,0.1347775],"genre_scores_gemma":[0.1472859,0.007994524,0.8253366,0.005396268,0.0003209416,0.001864134,0.0002386756,0.0005266592,0.01103626],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1315483,"threshold_uncertainty_score":0.6957022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02112932778170054,"score_gpt":0.2762589012935963,"score_spread":0.2551295735118958,"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."}}