{"id":"W4282841033","doi":"10.37663/0131-6184-2022-3-34-39","title":"Development of innovative infrastructure for individual fishing","year":2022,"lang":"en","type":"article","venue":"Fisheries","topic":"Food Industry and Aquatic Biology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Maritime Fishermen's Union","funders":"","keywords":"Fishing; Unitary state; Legislation; Business; Task (project management); Transportation infrastructure; Fish <Actinopterygii>; Environmental planning; Computer science; Fishery; Engineering; Transport engineering; Geography; Political science; Systems engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001597218,0.00005772843,0.00009759092,0.000005314752,0.0002930174,0.0000116204,0.0001773919,0.00004387394,0.001225682],"category_scores_gemma":[0.00005162053,0.00002357656,0.00001954482,0.0002436025,0.000045149,0.00005053535,0.0001422107,0.0001057687,4.326823e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001091554,"about_ca_system_score_gemma":0.00002195533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008345892,"about_ca_topic_score_gemma":0.00004249129,"domain_scores_codex":[0.9994953,0.00002753379,0.000162388,0.0001065418,0.00009259838,0.000115658],"domain_scores_gemma":[0.9997507,0.00008979657,0.00008877212,0.00001841012,0.0000383474,0.00001397159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001511415,0.00009876517,0.0458159,0.00002554248,0.0000936682,9.827264e-7,0.007339703,0.00001510261,0.1330988,0.002054899,0.04533744,0.765968],"study_design_scores_gemma":[0.0001035125,0.0003688766,0.2056261,0.000003502954,0.000002426373,0.0000026298,0.006952086,0.000004757469,0.01239161,0.001019791,0.7734135,0.0001111777],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979137,0.00001123917,0.000007211776,0.001119709,0.0001150498,0.0001115697,0.0002072055,0.00001456074,0.000499823],"genre_scores_gemma":[0.9964933,2.0143e-7,0.00264966,0.0002752864,0.00006510744,0.00006692058,0.0003109803,3.719815e-7,0.0001382102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7658569,"threshold_uncertainty_score":0.9996873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05160798484074897,"score_gpt":0.2297181881419171,"score_spread":0.1781102033011681,"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."}}