{"id":"W2101511566","doi":"10.1142/s1464333213500221","title":"WHAT INFLUENCES VALUED ECOSYSTEM COMPONENT SELECTION FOR CUMULATIVE EFFECTS IN IMPACT ASSESSMENT?","year":2013,"lang":"en","type":"article","venue":"Journal of Environmental Assessment Policy and Management","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Selection (genetic algorithm); Cumulative effects; Component (thermodynamics); Residual; Process (computing); Computer science; Biology; Ecology; Artificial intelligence; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006980374,0.0004098487,0.0005428325,0.0002757622,0.0002340647,0.0002801202,0.0002948148,0.000109433,0.0004885407],"category_scores_gemma":[0.0000103299,0.0003365823,0.000240553,0.0002401317,0.000165473,0.002786582,0.0003517836,0.0002776308,0.00003157646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002900864,"about_ca_system_score_gemma":0.00002604301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003601401,"about_ca_topic_score_gemma":0.00005185785,"domain_scores_codex":[0.9971151,0.0002565921,0.0008621458,0.0003888459,0.0007798598,0.0005974399],"domain_scores_gemma":[0.998675,0.0001717359,0.0006666778,0.0001696932,0.000006260421,0.0003105641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001569248,0.00320566,0.8847687,0.0003389625,0.0009707144,0.00004218474,0.00169493,0.01081542,0.04084622,0.00252575,0.001433977,0.05320061],"study_design_scores_gemma":[0.002570699,0.001046043,0.9851496,0.0001662957,0.0001044493,0.00002349954,0.001684436,0.003890344,0.0002210295,0.003877614,0.0009095404,0.0003564115],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941047,0.00008075334,0.001482927,0.0004839663,0.0003077612,0.001901913,0.00001220789,0.00001237805,0.001613382],"genre_scores_gemma":[0.9910374,0.001158049,0.006614605,0.0004436926,0.0001353939,0.0001935342,0.00001281828,0.00003326572,0.0003712382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.100381,"threshold_uncertainty_score":0.9999086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01034455324332658,"score_gpt":0.32695536755705,"score_spread":0.3166108143137235,"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."}}