{"id":"W4393429433","doi":"10.5281/zenodo.2025778","title":"Krill production circuit's - DATASET","year":2018,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Krill; Production (economics); Fishery; Oceanography; Biology; Geology; Economics; Microeconomics","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":[0.0008869814,0.002909448,0.001333903,0.00356211,0.0007879437,0.002248171,0.003701419,0.002154549,0.07716283],"category_scores_gemma":[0.00668704,0.0009140401,0.001845277,0.005644295,0.0004655087,0.001720886,0.001545031,0.001768596,0.09328092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001629733,"about_ca_system_score_gemma":0.002460631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03340945,"about_ca_topic_score_gemma":0.05564062,"domain_scores_codex":[0.9987631,0.0001703177,0.0001478857,0.0004655708,0.0002800814,0.0001731783],"domain_scores_gemma":[0.9977616,0.0006735509,0.0001650559,0.0007366369,0.0004974583,0.000165707],"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":[0.0001522099,0.00003353259,0.001219617,0.001098303,0.00008598434,0.00004256603,0.00002393697,0.001299363,0.0002357426,0.0005942608,0.9916589,0.003555652],"study_design_scores_gemma":[0.0004724359,0.00004347088,0.004439062,0.0002911329,0.00007496167,0.00009429544,0.00007230807,0.002039287,0.0007572774,0.001496968,0.9901626,0.00005617197],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001757074,0.0000638952,0.00008097589,0.00003212176,0.0000199879,0.000009996377,0.9981791,0.000903661,0.0005346052],"genre_scores_gemma":[0.0003704415,0.00003303808,0.000251033,0.00002080144,0.000002461708,0.00003419261,0.9989254,0.000106413,0.0002562172],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07716283,"threshold_uncertainty_score":0.2581354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04941003097231952,"score_gpt":0.2471640279330441,"score_spread":0.1977539969607246,"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."}}