{"id":"W2277404124","doi":"","title":"Drivers of Investment in Fisheries: An Econometric Application to Selected European Fisheries","year":2008,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fishery; Investment (military); Econometric model; Fisheries science; Fisheries management; Business; Fishing; Economics; Econometrics; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003341681,0.0006524387,0.001037727,0.001983634,0.000567114,0.002182317,0.00100363,0.001393347,0.005208439],"category_scores_gemma":[0.008439759,0.0007155252,0.002642703,0.00327362,0.0007328846,0.00139694,0.001153624,0.001434379,0.000457237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001552984,"about_ca_system_score_gemma":0.001103556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06690975,"about_ca_topic_score_gemma":0.05316912,"domain_scores_codex":[0.999384,0.0003132436,0.00004617414,0.0001189722,0.00004872215,0.00008881145],"domain_scores_gemma":[0.9902387,0.008116788,0.0006898257,0.000263714,0.0003293493,0.0003616003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008321693,0.0003939233,0.7016627,0.0002618527,0.002497871,0.001302807,0.0008318392,0.252728,0.0006690192,0.01536776,0.005304606,0.01814745],"study_design_scores_gemma":[0.0002815271,0.0003261048,0.5585043,0.000159017,0.001372499,0.0002953716,0.003400326,0.4181467,0.001123403,0.01012557,0.00610118,0.000163972],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907285,0.00064948,0.005023363,0.0004194734,0.00001966055,0.00003024341,0.002444471,0.00005088512,0.0006339832],"genre_scores_gemma":[0.9919591,0.0005645393,0.001706178,0.00004239084,0.00002992186,0.00004316488,0.003174252,0.00002429113,0.002456172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06690975,"threshold_uncertainty_score":0.1330405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01735043860546568,"score_gpt":0.2160685098194353,"score_spread":0.1987180712139697,"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."}}