{"id":"W4386221342","doi":"10.53555/sfs.v10i1.1513","title":"Socio - Economic Conditions Of Fishermen Community","year":2023,"lang":"en","type":"article","venue":"Journal of Survey in Fisheries Sciences","topic":"Fisheries and Aquaculture Studies","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fishing; Livelihood; Earnings; Sanitation; Socioeconomics; Business; Agriculture; Per capita income; Geography; Economic growth; Agricultural economics; Fishery; Economics; Engineering; Finance; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002384499,0.0000980058,0.0001601796,0.001213741,0.001644688,0.0007799014,0.0001629526,0.000179016,0.007470246],"category_scores_gemma":[0.001000423,0.00007571644,0.0001542474,0.0009255648,0.0004274485,0.0004370335,0.0008720601,0.0002425247,0.0007261065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008393616,"about_ca_system_score_gemma":0.0009577405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03936786,"about_ca_topic_score_gemma":0.1079842,"domain_scores_codex":[0.9996251,0.00005490421,0.00002400038,0.00003280065,0.00009889385,0.000164386],"domain_scores_gemma":[0.9994728,0.00005728,0.000137278,0.00001269881,0.000108771,0.000211248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003551914,0.0001331361,0.9859686,0.00003687022,0.00001290799,0.0007207444,0.004015139,0.00004876459,0.0002841741,0.000412697,0.0008113391,0.007520016],"study_design_scores_gemma":[9.069676e-7,0.00004646415,0.9879117,0.00001614646,0.000003659463,0.0001537192,0.01024051,0.00003349775,0.00003382591,0.00005709055,0.001497287,0.00000516624],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902701,0.0002228934,0.00001747062,0.0002276143,0.000008011711,0.00002091959,0.0005267966,0.000001372272,0.008704782],"genre_scores_gemma":[0.9969494,0.0002919321,0.00002393111,0.00004371256,0.000009706795,0.00001574931,0.0002258029,8.473287e-7,0.002438936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03936786,"threshold_uncertainty_score":0.07827741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2211525138731596,"score_gpt":0.2870862004206569,"score_spread":0.06593368654749732,"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."}}