{"id":"W2991440093","doi":"10.1109/smc.2019.8914029","title":"Visual fingerprinting for lobsters using deep learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Exoskeleton; Deep learning; Fishery; Human–computer interaction; Machine learning; Biology; Simulation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001515872,0.00006356921,0.00005750926,0.00003003491,0.00008413076,0.00004001896,0.00007741158,0.00006095149,0.00008902191],"category_scores_gemma":[0.00007762593,0.00006599115,0.0000597761,0.0000437625,0.00001512802,0.000002942793,0.00003235265,0.00003597993,0.00007467125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006761972,"about_ca_system_score_gemma":0.00001578725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000237415,"about_ca_topic_score_gemma":0.000001846492,"domain_scores_codex":[0.9994361,0.00001923484,0.0001398553,0.0002237343,0.00005408617,0.0001270003],"domain_scores_gemma":[0.9996654,0.0000129019,0.00007047498,0.0001386705,0.0000864075,0.00002617977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001307968,0.00001429992,0.00385761,0.00001623944,0.00001521987,2.061836e-8,0.00002986387,0.0006835263,0.9922006,0.0006061706,0.00007818865,0.002485227],"study_design_scores_gemma":[0.0004967487,0.0001041622,0.001561534,0.000009008059,0.00001258596,0.000004269898,0.0005849954,0.03917094,0.8715559,0.00001314453,0.08625336,0.0002333613],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9017079,0.00004215175,0.09588696,0.00003940043,0.0002641868,0.0001738238,5.901838e-7,0.00001802606,0.001866935],"genre_scores_gemma":[0.9891136,0.000005059415,0.003625849,0.0001181115,0.00007723495,0.00000930335,0.00006248243,0.00001505181,0.006973361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1206447,"threshold_uncertainty_score":0.2691041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809534940651012,"score_gpt":0.3035841517338741,"score_spread":0.285488802327364,"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."}}