{"id":"W4387641602","doi":"10.1016/j.scitotenv.2023.167456","title":"A process framework for integrating stressor-response functions into cumulative effects models","year":2023,"lang":"en","type":"review","venue":"The Science of The Total Environment","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Ministry of Environment; Institut National de la Recherche Scientifique; Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada","keywords":"Stressor; Cumulative effects; Identification (biology); Population; Process (computing); Risk analysis (engineering); Computer science; Psychology; Ecology; Biology; Medicine; Clinical psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.01065442,0.002710802,0.001409148,0.002739049,0.001174491,0.003559188,0.004646669,0.00270327,0.006989196],"category_scores_gemma":[0.02042291,0.001103436,0.003757166,0.002259081,0.002000141,0.003055702,0.003875126,0.004101263,0.001805685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002335885,"about_ca_system_score_gemma":0.00336639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01509815,"about_ca_topic_score_gemma":0.01193356,"domain_scores_codex":[0.9961576,0.002162332,0.0002658006,0.0005579923,0.0006296083,0.0002267526],"domain_scores_gemma":[0.989475,0.007752987,0.0007037188,0.0006057856,0.001175632,0.000286841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002455762,0.00007503511,0.001466659,0.0001606912,0.0001462653,0.0001644567,0.0003489748,0.4334141,0.0008845484,0.5405114,0.001653837,0.02114945],"study_design_scores_gemma":[0.0000178878,0.0000472359,0.0002677592,0.00004626607,0.00005477078,0.00004654479,0.0000421749,0.6608181,0.0003253041,0.3273051,0.0109926,0.00003625953],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0006847948,0.0001306846,0.9966815,0.0002499859,0.00002505438,0.00006566147,0.0001412073,0.0001923012,0.001828853],"genre_scores_gemma":[0.1479493,0.0016147,0.8398587,0.0004101723,0.0003162434,0.001833425,0.0008066756,0.000374832,0.006836002],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01509815,"threshold_uncertainty_score":0.05634665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06899651933501756,"score_gpt":0.3407988966051777,"score_spread":0.2718023772701602,"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."}}