{"id":"W6925562214","doi":"10.17895/ices.pub.19265252.v1","title":"Report of the Working Group on the Application of Genetics in Fisheries and Mariculture (WGAGFM)","year":2002,"lang":"en","type":"report","venue":"Figshare","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mariculture; Group (periodic table); Fish <Actinopterygii>; Work (physics); Aquaculture","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":[0.009386794,0.002003072,0.00120646,0.004794457,0.001733219,0.002543047,0.002459074,0.001287387,0.07723304],"category_scores_gemma":[0.009907241,0.0005998342,0.0008145556,0.003887452,0.0008430338,0.001208281,0.00186161,0.001795829,0.0331216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003885719,"about_ca_system_score_gemma":0.02609326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.240181,"about_ca_topic_score_gemma":0.2502642,"domain_scores_codex":[0.997521,0.0005281304,0.0001169697,0.0002692708,0.001270388,0.0002942075],"domain_scores_gemma":[0.9791279,0.002202986,0.000603615,0.0012586,0.01305052,0.003756448],"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":[0.0001181097,0.00007171452,0.001194092,0.0001308463,0.00001659815,0.0000319471,0.00009061192,0.0001979422,0.0002774928,0.0007068907,0.9745265,0.02263729],"study_design_scores_gemma":[0.0001175903,0.00008735938,0.01907743,0.000231456,0.00007369831,0.0000811945,0.0002902024,0.0004636908,0.001914778,0.001065436,0.9765555,0.00004158938],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0107504,0.01367349,0.03319818,0.04974481,0.01979218,0.004510031,0.6410487,0.005611047,0.2216713],"genre_scores_gemma":[0.01800802,0.008860681,0.04907463,0.00332419,0.002556352,0.003337355,0.3271648,0.002851912,0.5848221],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.240181,"threshold_uncertainty_score":0.4775661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1571355801211041,"score_gpt":0.3558157031585857,"score_spread":0.1986801230374816,"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."}}