{"id":"W6887301540","doi":"10.15468/inygc6","title":"International Barcode of Life project (iBOL)","year":2016,"lang":"en","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Barcode; Biodiversity; Investment (military); Data collection; Data access","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005269632,0.0002920153,0.0004954499,0.0003445899,0.00003137783,0.0001729766,0.003454983,0.0002349843,0.05100868],"category_scores_gemma":[0.0005754823,0.0002261213,0.00009876019,0.0001506743,0.0001265057,0.0003232823,0.001570152,0.0002370684,0.05048518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001256704,"about_ca_system_score_gemma":0.001153874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007610368,"about_ca_topic_score_gemma":0.0005253881,"domain_scores_codex":[0.9979032,0.0001243106,0.0005243018,0.0005737453,0.0006211098,0.0002533556],"domain_scores_gemma":[0.9980111,0.00009601551,0.000641786,0.0009748891,0.0001789717,0.00009725166],"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":[0.0001821898,0.0001365008,0.00001483425,0.00001656286,0.0002252066,0.00001677404,0.00002436351,2.495781e-7,0.0003654948,9.999083e-7,0.9937732,0.005243574],"study_design_scores_gemma":[0.0008631545,0.00004254671,0.00002259042,0.0002782023,0.00006484963,0.000008256748,0.00002090365,0.000001956529,0.0004829784,0.000005584749,0.9979208,0.0002882087],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001439974,0.00002368065,0.00000116618,0.00007124286,0.0004902555,0.0008346031,0.9788917,0.000001193507,0.01954212],"genre_scores_gemma":[0.00002316935,0.00003552762,0.0009746184,0.00002444354,0.0003941661,0.00005932375,0.9942549,0.00005738068,0.004176476],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01536565,"threshold_uncertainty_score":0.9502541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07312417397577463,"score_gpt":0.3763584785993777,"score_spread":0.303234304623603,"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."}}