{"id":"W2738186106","doi":"","title":"Inventory and Mapping of the Seaweed Resources in Québec","year":2014,"lang":"en","type":"article","venue":"144th Annual Meeting of the American Fisheries Society","topic":"Marine and coastal plant biology","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Algae; Environmental science; Computer science; Ecology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003057261,0.0002773114,0.0002101021,0.003609939,0.001095006,0.0008039265,0.0006909878,0.0002438003,0.003771206],"category_scores_gemma":[0.0008813764,0.0001540472,0.0002453026,0.005910365,0.0002379146,0.0003018214,0.0005186635,0.0002404761,0.0003743148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01514594,"about_ca_system_score_gemma":0.01294102,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9954554,"about_ca_topic_score_gemma":0.9973839,"domain_scores_codex":[0.9997849,0.00001909222,0.00001474682,0.00005105105,0.00007640384,0.00005377492],"domain_scores_gemma":[0.9990427,0.00007122493,0.0001181152,0.00004282637,0.0006182333,0.0001068947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001952039,0.00008578831,0.8122501,0.000314514,0.0003443786,0.0003608384,0.002263543,0.00667152,0.00575625,0.003482603,0.01547019,0.1528051],"study_design_scores_gemma":[0.00001021524,0.00001455513,0.9669533,0.0001047614,0.00003226506,0.00006368244,0.0009312072,0.005630718,0.0003971379,0.00009450943,0.02575081,0.00001689363],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9143623,0.002832161,0.002736819,0.0006041936,0.00001760296,0.0002133128,0.05753589,0.0001653376,0.02153251],"genre_scores_gemma":[0.9745255,0.0008561517,0.003817828,0.00008550352,0.000004765257,0.00007129745,0.01008828,0.00002174937,0.01052897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01514594,"threshold_uncertainty_score":0.109892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008428207272951451,"score_gpt":0.1798141224759469,"score_spread":0.1713859152029955,"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."}}