{"id":"W2322115106","doi":"10.5751/es-06656-190273","title":"Resource degradation, marginalization, and poverty in small-scale fisheries: threats to social-ecological resilience in India and Brazil","year":2014,"lang":"en","type":"article","venue":"Ecology and Society","topic":"Innovation and Socioeconomic Development","field":"Business, Management and Accounting","cited_by":158,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; University of Waterloo; International Development Research Centre; Canada Excellence Research Chairs, Government of Canada; Canada Research Chairs","keywords":"Poverty; Environmental degradation; Ecological resilience; Resilience (materials science); Scale (ratio); Resource (disambiguation); Psychological resilience; Fishery; Geography; Environmental resource management; Ecology; Economics; Economic growth; Ecosystem; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007079754,0.00009163373,0.0001646761,0.00006480113,0.0002597637,0.00007775218,0.00005658516,0.0001540219,0.00006615514],"category_scores_gemma":[0.0001236602,0.00009050466,0.00001297715,0.0002099605,0.000122083,0.0002031298,0.0001150454,0.00010564,0.00000627636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000432897,"about_ca_system_score_gemma":0.00002139213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004031502,"about_ca_topic_score_gemma":0.0008627457,"domain_scores_codex":[0.9993047,0.00002595981,0.0002109993,0.0002459802,0.00003526478,0.0001770896],"domain_scores_gemma":[0.9997514,0.00008400386,0.00006925865,0.0000452676,0.00003599457,0.00001405368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001084344,0.00002827506,0.9635129,0.00002961958,0.000004394299,6.389139e-7,0.001919768,0.000003331411,0.000006980443,0.01908232,0.01327185,0.002129088],"study_design_scores_gemma":[0.0005402027,0.00001036029,0.9728642,0.000005282061,0.000002850037,6.75548e-7,0.002025609,0.0005015085,0.000001325417,0.004124516,0.01981122,0.0001122754],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848486,0.00001130961,0.0001081529,0.007933132,0.0000478539,0.0001653049,7.276341e-7,0.00001834918,0.006866562],"genre_scores_gemma":[0.9657304,0.00003376395,0.0005083664,0.03292546,0.00007122377,0.00002691009,0.00001497377,0.000005827156,0.0006830451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02499233,"threshold_uncertainty_score":0.3690673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0100880560283718,"score_gpt":0.2183103586814323,"score_spread":0.2082223026530605,"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."}}