{"id":"W6893685289","doi":"10.5281/zenodo.3843148","title":"The BenBioDen database, a global database for meio-, macro- and megabenthic biomass and densities - R code","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Memorial University of Newfoundland; Université Laval; St. Francis Xavier University; Fisheries and Oceans Canada; Université du Québec à Chicoutimi","funders":"European Commission","keywords":"Biomass (ecology); Code (set theory); Production (economics); Source code","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":[],"consensus_categories":[],"category_scores_codex":[0.003035619,0.003612105,0.003545627,0.004526242,0.0007664775,0.004319074,0.005562432,0.002066423,0.06677758],"category_scores_gemma":[0.01462008,0.001833594,0.002092384,0.005747516,0.0005932587,0.002541486,0.002902295,0.003018008,0.0818368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009617937,"about_ca_system_score_gemma":0.002976747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005850687,"about_ca_topic_score_gemma":0.00449725,"domain_scores_codex":[0.9978364,0.0004467742,0.0003246717,0.0005979038,0.000632068,0.0001622063],"domain_scores_gemma":[0.9954457,0.001892059,0.0004921156,0.001025608,0.000875165,0.000269314],"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":[0.0006740186,0.00006332269,0.003392864,0.004568572,0.0007414134,0.0002667409,0.0001837202,0.005506723,0.006921492,0.01189678,0.9382331,0.02755121],"study_design_scores_gemma":[0.0005329326,0.0000546574,0.004016224,0.0005410946,0.0003553555,0.0003781095,0.00008497503,0.009079528,0.008935311,0.02334881,0.9524907,0.0001822211],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.001486285,0.001180554,0.04364478,0.0003270918,0.0002322619,0.0001205924,0.8813476,0.0658162,0.005844661],"genre_scores_gemma":[0.007350568,0.001026258,0.03837188,0.000349333,0.00006782256,0.0006454922,0.9236326,0.02615423,0.002401781],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.06677758,"threshold_uncertainty_score":0.2233933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02393293845948063,"score_gpt":0.2372024964713622,"score_spread":0.2132695580118816,"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."}}