{"id":"W2748724358","doi":"10.1071/aj13070","title":"Automating the process of net environmental benefit analysis (NEBA) for emergency response and environmental plans","year":2014,"lang":"en","type":"article","venue":"The APPEA Journal","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"ConocoPhillips (Canada)","funders":"","keywords":"Process (computing); Oil spill; Emergency response; Computer science; Table (database); Risk analysis (engineering); Operations research; Environmental science; Engineering; Data mining; Business; Environmental engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001492197,0.0002021618,0.0002441093,0.00003956564,0.0008575973,0.00004227152,0.0004696645,0.00006020124,0.001096992],"category_scores_gemma":[0.00003226017,0.0001206343,0.0001997807,0.000144038,0.0004228385,0.0002223708,0.0002042206,0.0002199001,0.00002200759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001281096,"about_ca_system_score_gemma":0.000004496842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002258586,"about_ca_topic_score_gemma":0.00002382191,"domain_scores_codex":[0.9982431,0.0002372349,0.0004341494,0.0002150069,0.0005127789,0.0003577432],"domain_scores_gemma":[0.9990054,0.000203118,0.0003918947,0.0002619362,0.000001241307,0.0001364401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000336312,0.0002519105,0.9160233,0.00001023765,0.0004702693,0.000001654209,0.006506868,0.005058916,0.05614917,0.0000360636,0.0002615457,0.01489383],"study_design_scores_gemma":[0.000520638,0.0002522265,0.9845016,0.000007713375,0.0004166111,0.00004091558,0.003274849,0.007442867,0.001221104,0.001510355,0.0006160273,0.0001950827],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974127,0.00009391933,0.001224515,0.0005337201,0.00007714322,0.0002766769,0.00008304161,0.000008805941,0.000289505],"genre_scores_gemma":[0.9990249,0.0001387602,0.0001987699,0.0001367677,0.00008949848,0.00001938899,0.00001501526,0.00002005133,0.0003568612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06847838,"threshold_uncertainty_score":0.9998161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006965672784926684,"score_gpt":0.25766055826577,"score_spread":0.2506948854808433,"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."}}