{"id":"W2095656484","doi":"10.1080/14615517.2015.1039382","title":"Selection of valued ecosystem components in cumulative effects assessment: lessons from Canadian road construction projects","year":2015,"lang":"en","type":"article","venue":"Impact Assessment and Project Appraisal","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Selection (genetic algorithm); Context (archaeology); Cumulative effects; Process (computing); Environmental resource management; Component (thermodynamics); Computer science; Environmental planning; Operations research; Geography; Environmental science; Engineering; Ecology; Artificial intelligence; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02462797,0.0005567786,0.000423813,0.003473372,0.005753887,0.006182525,0.002065474,0.0006782648,0.001434405],"category_scores_gemma":[0.04083031,0.0003481315,0.0004145721,0.007407872,0.004163845,0.003175068,0.003870042,0.001476409,0.0001567728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04819348,"about_ca_system_score_gemma":0.06071002,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9133198,"about_ca_topic_score_gemma":0.9709402,"domain_scores_codex":[0.9880776,0.004988829,0.0004651845,0.0005343473,0.004782001,0.001152049],"domain_scores_gemma":[0.9628118,0.01736714,0.001435601,0.001819329,0.01518573,0.001380511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002118609,0.0001810075,0.1291401,0.001280235,0.00009365196,0.001616368,0.1700809,0.01017528,0.002113522,0.03827651,0.01104721,0.6357834],"study_design_scores_gemma":[0.00005006082,0.0002842419,0.3791634,0.003272246,0.0002130086,0.0007396539,0.3714487,0.01452343,0.003907557,0.03292404,0.1931438,0.0003299295],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8345644,0.006231284,0.03195483,0.01245744,0.00008218983,0.0007868094,0.0007002731,0.0001010399,0.1131217],"genre_scores_gemma":[0.96522,0.00361256,0.02681121,0.0002932953,0.00001041154,0.0001547754,0.0002495311,0.00004541526,0.00360282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08668023,"threshold_uncertainty_score":0.3496698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04562866131505549,"score_gpt":0.3849727119913425,"score_spread":0.339344050676287,"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."}}