{"id":"W4230361395","doi":"10.5194/essd-2019-160","title":"Green Edge ice camp campaigns: understanding the processes controlling the under-ice Arctic phytoplankton spring bloom","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"NanoQuébec (Canada); University of Calgary; Institut National de Santé Publique du Québec; University of Manitoba; Université du Québec; GDG Environnement; Université du Québec à Rimouski; Université Laval","funders":"Institut Polaire Français Paul Emile Victor; Centre National de la Recherche Scientifique; Centre National d’Etudes Spatiales; Canada Research Chairs; Agence Nationale de la Recherche; University of Manitoba; ArcticNet; Université Laval","keywords":"Oceanography; Phytoplankton; Environmental science; Sea ice; Spring bloom; Bloom; Bay; Arctic; Ecology; Geology; Nutrient; Biology","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":[0.001026472,0.0004086921,0.0003370241,0.0008250115,0.0004173206,0.0006418675,0.0004385148,0.0003587741,0.001444334],"category_scores_gemma":[0.0008808774,0.0001539268,0.0003049725,0.001017988,0.000149863,0.0004986884,0.0007263353,0.000378112,0.0008282402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006631704,"about_ca_system_score_gemma":0.000917838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1174678,"about_ca_topic_score_gemma":0.2304003,"domain_scores_codex":[0.9997292,0.00005581411,0.00001207145,0.00007214332,0.00005947368,0.0000712842],"domain_scores_gemma":[0.9992835,0.00008714142,0.0001188026,0.0001148475,0.0002177417,0.0001780739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001154165,0.0004871637,0.8625752,0.0002874916,0.0002674041,0.0003081647,0.001363163,0.004564186,0.007386115,0.0007871242,0.070295,0.05052492],"study_design_scores_gemma":[0.00006119022,0.000076474,0.9675468,0.0000658198,0.00003943285,0.00002153028,0.0006356827,0.004681638,0.001557386,0.0001801801,0.0251156,0.00001832017],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8444367,0.0005810498,0.001048893,0.0003057345,0.00009757761,0.00009885974,0.1450367,0.0002637665,0.008130705],"genre_scores_gemma":[0.6258504,0.0004071443,0.005947004,0.0002238283,0.0001297772,0.0002268093,0.363152,0.0001378345,0.003925324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1174678,"threshold_uncertainty_score":0.2335681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0342543449696404,"score_gpt":0.2267997095240062,"score_spread":0.1925453645543658,"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."}}