{"id":"W2569953748","doi":"10.1186/s40152-016-0055-z","title":"Having it all: can fisheries buybacks achieve capacity, economic, ecological, and social objectives?","year":2017,"lang":"en","type":"article","venue":"MAST. Maritime studies/Maritime studies","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Fishery; Business; Social benefits; Natural resource economics; Ecology; Economics; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.01503502,0.0002748372,0.0004978663,0.00108857,0.001153473,0.003945911,0.000813245,0.001124186,0.002461375],"category_scores_gemma":[0.03894001,0.0001708627,0.0003864758,0.001303871,0.002538658,0.005759168,0.001519516,0.001086984,0.0002227702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003483673,"about_ca_system_score_gemma":0.005141557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01957113,"about_ca_topic_score_gemma":0.03880407,"domain_scores_codex":[0.9942759,0.003077233,0.0003505897,0.0002582462,0.001219069,0.0008190197],"domain_scores_gemma":[0.9792579,0.01238319,0.003624788,0.0004722341,0.003138746,0.00112315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004291989,0.0006335416,0.1168839,0.01379361,0.0005498276,0.001191758,0.05123634,0.001275374,0.002362998,0.03304445,0.006201078,0.7723979],"study_design_scores_gemma":[0.0001005798,0.002340801,0.5041171,0.02087055,0.0009963007,0.0009089727,0.3068283,0.001589626,0.004862493,0.02725023,0.129912,0.0002229708],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8927674,0.03716602,0.002523101,0.01820009,0.0004464192,0.0002694533,0.000173164,0.00003336046,0.048421],"genre_scores_gemma":[0.9879071,0.007813999,0.001217611,0.001160582,0.00006178333,0.00007520181,0.0000532669,0.000008348181,0.001702049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01957113,"threshold_uncertainty_score":0.07951373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07477055959793374,"score_gpt":0.3215332217833823,"score_spread":0.2467626621854486,"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."}}