{"id":"W2982604179","doi":"10.3389/fmars.2019.00653","title":"Perspectives on in situ Sensors for Ocean Acidification Research","year":2019,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Ocean Acidification Effects and Responses","field":"Earth and Planetary Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Tula Foundation; Fisheries and Oceans Canada; University of Victoria; University of Calgary; Ocean Networks Canada Society","funders":"Hakai Institute","keywords":"Metadata; Quality assurance; Limiting; Ocean acidification; Software deployment; Reliability (semiconductor); Computer science; Data quality; Ocean observations; Process (computing); Best practice; Quality (philosophy); Control (management); Data science; Environmental science; Environmental resource management; Process management; Oceanography; Engineering; Operations management; Geography; World Wide Web; Software engineering; Seawater; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.05883462,0.001388488,0.001459614,0.001934344,0.003187163,0.01110587,0.003724501,0.01107435,0.01196061],"category_scores_gemma":[0.02509193,0.0005442983,0.001488632,0.003160042,0.009050515,0.01556602,0.007476482,0.01339956,0.002194297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008996194,"about_ca_system_score_gemma":0.01371338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02066203,"about_ca_topic_score_gemma":0.02460953,"domain_scores_codex":[0.9776825,0.01307919,0.000882102,0.001738273,0.004751423,0.001866349],"domain_scores_gemma":[0.9382879,0.03700617,0.001908026,0.002233324,0.01657323,0.003991398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005735485,0.0003554288,0.00663873,0.009883268,0.0001459217,0.0009102983,0.02689994,0.003750605,0.013687,0.380777,0.2783149,0.2780634],"study_design_scores_gemma":[0.00001742259,0.0001693065,0.002163341,0.002402217,0.00003618137,0.0001909775,0.02207499,0.000430357,0.002284588,0.07843308,0.8917139,0.00008368682],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.005064959,0.1232575,0.0156343,0.8125531,0.01116036,0.00007870773,0.0007786613,0.00012461,0.0313479],"genre_scores_gemma":[0.1860664,0.4424681,0.06116475,0.2464917,0.02597232,0.0006083149,0.001820185,0.0003749754,0.03503329],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.05883462,"threshold_uncertainty_score":0.311151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02124925891866127,"score_gpt":0.2884639900354444,"score_spread":0.2672147311167832,"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."}}