{"id":"W3025411580","doi":"10.3389/fmars.2020.00303","title":"Good Practices for Species Distribution Modeling of Deep-Sea Corals and Sponges for Resource Management: Data Collection, Analysis, Validation, and Communication","year":2020,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"National Oceanic and Atmospheric Administration","keywords":"Data collection; Context (archaeology); Spatial analysis; Sampling (signal processing); Leverage (statistics); Computer science; Inference; Data science; Scale (ratio); Resource (disambiguation); Environmental resource management; Ecology; Data mining; Geography; Environmental science; Cartography; Machine learning; Remote sensing; Statistics; Biology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08983516,0.002174047,0.00200209,0.005949383,0.002435356,0.006000492,0.007632954,0.00302428,0.003511585],"category_scores_gemma":[0.2172177,0.002719767,0.00324512,0.006984058,0.001932522,0.006633059,0.004883841,0.005693531,0.004726953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002215192,"about_ca_system_score_gemma":0.006434995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01975076,"about_ca_topic_score_gemma":0.0270973,"domain_scores_codex":[0.9478812,0.03039028,0.006997319,0.004193494,0.009957239,0.000580493],"domain_scores_gemma":[0.8073694,0.07915674,0.01449768,0.05559506,0.04149458,0.001886586],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000387394,0.0009218959,0.07823443,0.004046831,0.001861615,0.0009587435,0.006558199,0.1695103,0.02395272,0.05476619,0.11769,0.5411116],"study_design_scores_gemma":[0.000257313,0.0002420403,0.0266161,0.003295132,0.000476187,0.0007050526,0.001903727,0.5241768,0.03409648,0.1442618,0.2633958,0.0005736242],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002949384,0.0003524808,0.9851376,0.001071182,0.0001313185,0.0008899215,0.002879184,0.00505414,0.001534877],"genre_scores_gemma":[0.01685289,0.000355203,0.9766995,0.0003166253,0.00006781805,0.00211017,0.002439362,0.0007789811,0.0003795209],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9101648,"threshold_uncertainty_score":0.4750994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04070331496065889,"score_gpt":0.2725360935525399,"score_spread":0.231832778591881,"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."}}