{"id":"W3009151774","doi":"10.1371/journal.pone.0228912","title":"Valuing invisible catches: Estimating the global contribution by women to small-scale marine capture fisheries production","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Coastal and Marine Management","field":"Environmental Science","cited_by":183,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Fisheries and Oceans Canada","funders":"Social Sciences and Humanities Research Council of Canada; Paul M. Angell Family Foundation","keywords":"Fishing; Fishery; Subsistence agriculture; Livelihood; Scale (ratio); Geography; Food security; Business; Agricultural economics; Socioeconomics; Economics; Agriculture","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001928385,0.0006301884,0.0002509467,0.002133681,0.000264228,0.001054584,0.000482936,0.0004009723,0.002694177],"category_scores_gemma":[0.006627064,0.0002195595,0.0008240082,0.002936799,0.00071351,0.001608728,0.001691953,0.0004887396,0.0007749652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005984067,"about_ca_system_score_gemma":0.0003932069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0173887,"about_ca_topic_score_gemma":0.0178704,"domain_scores_codex":[0.9993626,0.0002431687,0.00005055995,0.0001143515,0.0001324799,0.00009690338],"domain_scores_gemma":[0.997695,0.000900797,0.0008389172,0.00014379,0.0003112153,0.0001101269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004581902,0.00001429604,0.968896,0.0001872879,0.0001475136,0.0001722744,0.001750151,0.0006499759,0.0001914428,0.001342556,0.001568248,0.02503436],"study_design_scores_gemma":[0.000009883195,0.0001178942,0.9525117,0.0004731746,0.0003283028,0.0005685811,0.01236457,0.004656714,0.0006936555,0.002953671,0.02527984,0.00004200176],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9607882,0.003927371,0.00658812,0.001520714,0.00006882451,0.0001105002,0.0116085,0.00003184051,0.01535593],"genre_scores_gemma":[0.9876272,0.002601289,0.003658626,0.0002754847,0.00005472091,0.0001684387,0.003414235,0.00002057757,0.002179424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0173887,"threshold_uncertainty_score":0.03457499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01840432824531693,"score_gpt":0.182528705227717,"score_spread":0.1641243769824001,"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."}}