{"id":"W6962941187","doi":"10.1594/pangaea.946919","title":"Abundance of epibenthic fauna on a Deep-Sea Cliff offshore Greenland and the Associated Epibenthic Fauna","year":2021,"lang":"en","type":"dataset","venue":"Publishing Network for Geoscientific and Environmental Data (PANGAEA) (Alfred Wegener Institute for Polar and Marine Research)","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020; Natural Environment Research Council","keywords":"Fauna; Abundance (ecology); Submarine pipeline; Cliff; Terrain; Abiotic component; Benthos; Benthic zone","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":[0.001134381,0.001537344,0.001266362,0.004149701,0.0008049039,0.001725678,0.002313224,0.001448226,0.03564004],"category_scores_gemma":[0.003005033,0.0005368523,0.0008416806,0.006109222,0.0004573613,0.0009039833,0.001911499,0.001416808,0.03897792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00176898,"about_ca_system_score_gemma":0.002120669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05586992,"about_ca_topic_score_gemma":0.1369338,"domain_scores_codex":[0.9993444,0.00007735188,0.00007148608,0.0002386834,0.0001361013,0.0001320718],"domain_scores_gemma":[0.9985493,0.0003178468,0.0001948289,0.0002651147,0.0004947909,0.0001780531],"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":[0.00009779775,0.0000413711,0.006707652,0.001162255,0.00006882546,0.00006013905,0.0001185467,0.0004455926,0.0003162522,0.000580388,0.9859923,0.004408931],"study_design_scores_gemma":[0.0002919571,0.00002737017,0.05284288,0.0007304036,0.00008201109,0.0001455913,0.0004565883,0.0008102978,0.0006649858,0.001134392,0.9427527,0.00006071134],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004534795,0.00002853481,0.0000329927,0.00002367646,0.000008211679,0.000007077054,0.9990737,0.00006983249,0.0003024001],"genre_scores_gemma":[0.0007356522,0.00002349981,0.0002068103,0.00001415332,0.000002473625,0.00005264463,0.998556,0.00002237265,0.0003864338],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05586992,"threshold_uncertainty_score":0.1192278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1237137218145186,"score_gpt":0.3480003489802508,"score_spread":0.2242866271657322,"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."}}