{"id":"W4206866540","doi":"","title":"Water Framework Directive Intercalibration: Central-Baltic Lake Fish fauna ecological assessment methods. Part B: Development of the intercalibration common metric; Part C: Intercalibration","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Environment","funders":"","keywords":"Water Framework Directive; Fish <Actinopterygii>; Directive; Environmental science; Fauna; Ecology; Fishery; Geography; Water quality; Biology; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01292701,0.001782129,0.001086995,0.004930486,0.001309565,0.004996582,0.003838742,0.003644531,0.01323568],"category_scores_gemma":[0.01704292,0.001356218,0.000988781,0.005253504,0.001304611,0.003729946,0.00382329,0.00393729,0.008827077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005330716,"about_ca_system_score_gemma":0.01336434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1171227,"about_ca_topic_score_gemma":0.09078028,"domain_scores_codex":[0.9839445,0.003278171,0.002025881,0.001057801,0.009104555,0.0005890584],"domain_scores_gemma":[0.9878057,0.001366555,0.001255792,0.001142015,0.008110761,0.0003192321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001668857,0.0004581264,0.008108375,0.00197433,0.0001164116,0.0002604256,0.001595284,0.004923046,0.00409833,0.04011821,0.5699995,0.368181],"study_design_scores_gemma":[0.00003951821,0.00007318314,0.01702794,0.0009307112,0.00003262967,0.00009162928,0.000270933,0.001134827,0.001644031,0.004771018,0.9739174,0.0000660501],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.01723568,0.01946112,0.176277,0.01285315,0.003786019,0.007561502,0.1172289,0.008436885,0.6371597],"genre_scores_gemma":[0.07018752,0.01613436,0.3769943,0.01210258,0.0005970581,0.01518929,0.2180688,0.005122288,0.2856038],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1171227,"threshold_uncertainty_score":0.2328818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02262849368177151,"score_gpt":0.2793278689314199,"score_spread":0.2566993752496484,"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."}}