{"id":"W2947203848","doi":"10.1080/14615517.2019.1601432","title":"Introducing SEA effectiveness","year":2019,"lang":"en","type":"article","venue":"Impact Assessment and Project Appraisal","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006378451,0.0002964985,0.0003198812,0.00005525895,0.0001633874,0.0001299799,0.0001800756,0.0001046524,0.002240879],"category_scores_gemma":[0.00002066458,0.0002345868,0.0001117703,0.0002621182,0.0001362364,0.0006616951,0.0003085803,0.0002665224,0.0003315683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003945008,"about_ca_system_score_gemma":0.00005360542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001051773,"about_ca_topic_score_gemma":0.00001944239,"domain_scores_codex":[0.9981313,0.0002250222,0.0002023932,0.0004880122,0.000447743,0.00050551],"domain_scores_gemma":[0.9991886,0.0002707992,0.00009709629,0.000282245,0.000004394007,0.0001568396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005775734,0.0001252029,0.9733182,0.00002243715,0.0000511235,0.000004003493,0.0003704434,0.0001548887,0.01841954,0.00008789042,0.0007077482,0.00668083],"study_design_scores_gemma":[0.001010188,0.0004980484,0.9942587,0.00002132389,0.00002621823,0.00001561768,0.0002644505,0.00100937,0.0006910699,0.0001575082,0.001696803,0.0003507242],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.965445,0.00002177022,0.0002686117,0.00008191939,0.0003958363,0.0006975505,0.00001437704,0.00006841598,0.03300653],"genre_scores_gemma":[0.9982377,0.00002738075,0.001198294,0.00007317851,0.0001062715,0.00003835839,0.00003476298,0.00003233648,0.0002516792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03279275,"threshold_uncertainty_score":0.9986712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01191526097262881,"score_gpt":0.3704230304747605,"score_spread":0.3585077695021317,"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."}}