{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.01721377,0.0005309893,0.0009401406,0.0006515671,0.002465608,0.003341302,0.002574041,0.0004331854,0.0001835834],"category_scores_gemma":[0.003858709,0.0003835167,0.0002239403,0.001045833,0.003251389,0.002160532,0.004857393,0.001109267,0.000009097062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001034959,"about_ca_system_score_gemma":0.0001489708,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.138987,"about_ca_topic_score_gemma":0.2640501,"domain_scores_codex":[0.9929071,0.0006897787,0.001175881,0.001899122,0.002268455,0.001059671],"domain_scores_gemma":[0.9944538,0.0020911,0.0007175287,0.002196735,0.0001927244,0.0003480586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003978974,0.0001247087,0.004272768,0.0002023352,0.0002139964,0.000004064572,0.00007486604,0.00005165293,0.000002413323,0.0007991733,0.9435396,0.05031654],"study_design_scores_gemma":[0.00207024,0.0001561246,0.005838317,0.0001593072,0.0001988999,0.00001720057,0.0003518666,0.009338178,0.000004172273,0.002548987,0.9789094,0.0004073498],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007480108,0.003772355,0.0006337984,0.001115498,0.002040282,0.001738773,0.9830099,0.00001888519,0.0001903884],"genre_scores_gemma":[0.006749008,0.007662504,0.0004378171,0.0002058081,0.0005058245,0.0001223023,0.9820141,0.00003713555,0.00226549],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.125063,"threshold_uncertainty_score":0.9998617,"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."}}