{"id":"W4285676373","doi":"10.1016/j.ecoleng.2022.106732","title":"Developing performance standards in fish passage: Integrating ecology, engineering and socio-economics","year":2022,"lang":"en","type":"article","venue":"Ecological Engineering","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Fish <Actinopterygii>; Process (computing); Set (abstract data type); Environmental resource management; Construct (python library); Fish migration; Ecology; Relevance (law); Environmental science; Risk analysis (engineering); Computer science; Engineering; Fishery; Business; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0006722886,0.0001473667,0.0002029289,0.00006313623,0.0002644131,0.00001606448,0.0001625917,0.00006101398,0.0007582547],"category_scores_gemma":[0.0001811278,0.0001544867,0.00002346788,0.0001622551,0.00004963152,0.0001432548,0.0009559385,0.0003740432,0.000006308719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009545416,"about_ca_system_score_gemma":0.00001176023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007823613,"about_ca_topic_score_gemma":0.0002766146,"domain_scores_codex":[0.9990177,0.00002294396,0.0002199361,0.0002800924,0.00008685765,0.0003724395],"domain_scores_gemma":[0.9997182,0.0001260373,0.00004128461,0.00007364874,0.00000331467,0.00003753744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000007811097,0.00003415616,0.4403179,0.00002868706,0.00002026308,0.00002443268,0.000248724,0.555496,0.00004787618,0.001749741,0.0009778221,0.001046588],"study_design_scores_gemma":[0.000272941,0.0001053972,0.8806856,0.000004959609,0.000004577931,0.000007963147,0.0002428236,0.1041424,0.00001487878,0.00008312501,0.01422012,0.0002151841],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967301,0.000008867011,0.0003783286,0.001026358,0.0002351767,0.0001746388,0.000006080252,0.00008757812,0.00135291],"genre_scores_gemma":[0.9961448,0.00008897568,0.002891078,0.0005792145,0.00001713891,0.0001849568,0.000003658829,0.0000115412,0.00007858693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4513535,"threshold_uncertainty_score":0.8302358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006870269604755464,"score_gpt":0.1884038691733586,"score_spread":0.1815335995686031,"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."}}