{"id":"W3208439977","doi":"10.5281/zenodo.1402033","title":"Dataset Of E. Huxleyi Blooms: Spatio-Temporal Distribution And Their Impact On High-Latitudinal Marine Environments (1998-2016)","year":2018,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Marine and environmental studies","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Oceanography; Environmental science; Distribution (mathematics); Algal bloom; Geography; Physical geography; Ecology; Geology; Biology; Phytoplankton; Mathematics; Nutrient","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":[0.0005450597,0.001275491,0.0009333618,0.0021649,0.0005064064,0.001196931,0.001507811,0.001023858,0.01984203],"category_scores_gemma":[0.001888838,0.0004033685,0.001044451,0.003798687,0.0002801143,0.0007529746,0.00168504,0.0009125759,0.02261144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001146111,"about_ca_system_score_gemma":0.001949024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03375064,"about_ca_topic_score_gemma":0.05627348,"domain_scores_codex":[0.9995576,0.00004629779,0.00005846837,0.0001437804,0.0001045254,0.00008935604],"domain_scores_gemma":[0.9991916,0.000114377,0.0001211228,0.0001798063,0.0002841021,0.0001090245],"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":[0.0001683262,0.00004409499,0.008113042,0.001036542,0.000105495,0.0000574515,0.00006350114,0.0007493078,0.000762495,0.0006424113,0.9832025,0.005054737],"study_design_scores_gemma":[0.0002133064,0.00002561768,0.04578052,0.0003434163,0.00007685587,0.00008596825,0.0002052062,0.0007341713,0.000865281,0.0006453253,0.9509823,0.00004206441],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005202311,0.00004051977,0.00004620386,0.00002868233,0.00001774021,0.000006322945,0.9988766,0.0001305152,0.0003331612],"genre_scores_gemma":[0.000655695,0.00003152911,0.0001817446,0.00001547343,0.000003711799,0.00002594024,0.9987364,0.00002172435,0.0003276732],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03375064,"threshold_uncertainty_score":0.06710839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02009566998735548,"score_gpt":0.2157928301330679,"score_spread":0.1956971601457125,"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."}}